Above are papers related to the following paper: [Paper_ID] Paper #3243. 	[Title] A System for Induction of Oblique Decision Trees [Abstract] This article describes a new system for induction of oblique decision trees. This system, OC1, combines deterministic hill-climbing with two forms of randomization to find a good oblique split (in the form of a hyperplane) at each node of a decision tree. Oblique decision tree methods are tuned especially for domains in which the attributes are numeric, although they can be adapted to symbolic or mixed symbolic/numeric attributes. We present extensive empirical studies, using both real and artificial data, that analyze OC1's ability to construct oblique trees that are smaller and more accurate than their axis-parallel counterparts. We also examine the benefits of randomization for the construction of oblique decision trees. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #8961. 	[Title] Machine Learning Research: Four Current Directions [Abstract] Machine Learning research has been making great progress is many directions. This article summarizes four of these directions and discusses some current open problems. The four directions are (a) improving classification accuracy by learning ensembles of classifiers, (b) methods for scaling up supervised learning algorithms, (c) reinforcement learning, and (d) learning complex stochastic models. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #45188. 	[Title] A Bound on the Error of Cross Validation Using the Approximation and Estimation Rates, with [Abstract] We give an analysis of the generalization error of cross validation in terms of two natural measures of the difficulty of the problem under consideration: the approximation rate (the accuracy to which the target function can be ideally approximated as a function of the number of hypothesis parameters), and the estimation rate (the deviation between the training and generalization errors as a function of the number of hypothesis parameters). The approximation rate captures the complexity of the target function with respect to the hypothesis model, and the estimation rate captures the extent to which the hypothesis model suffers from overfitting. Using these two measures, we give a rigorous and general bound on the error of cross validation. The bound clearly shows the tradeoffs involved with making fl the fraction of data saved for testing too large or too small. By optimizing the bound with respect to fl, we then argue (through a combination of formal analysis, plotting, and controlled experimentation) that the following qualitative properties of cross validation behavior should be quite robust to significant changes in the underlying model selection problem: Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #307336. 	[Title] A Support Vector Machine Approach to Decision Trees [Abstract] Key ideas from statistical learning theory and support vector machines are generalized to decision trees. A support vector machine is used for each decision in the tree. The "optimal" decision tree is characterized, and both a primal and dual space formulation for constructing the tree are proposed. The result is a method for generating logically simple decision trees with multivariate linear or nonlinear decisions. The preliminary results indicate that the method produces simple trees that generalize well with respect to other decision tree algorithms and single support vector machines. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1113739. 	[Title] Decision Tree Function Approximation in Reinforcement Learning [Abstract] We present a decision tree based approach to function approximation in reinforcement learning. We compare our approach with table lookup and a neural network function approximator on three problems: the well known mountain car and pole balance problems as well as a simulated automobile race car. We find that the decision tree can provide better learning performance than the neural network function approximation and can solve large problems that are infeasible using table lookup. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6163. 	[Title] Generalization in Reinforcement Learning: Safely Approximating the Value Function [Abstract] To appear in: G. Tesauro, D. S. Touretzky and T. K. Leen, eds., Advances in Neural Information Processing Systems 7, MIT Press, Cambridge MA, 1995. A straightforward approach to the curse of dimensionality in reinforcement learning and dynamic programming is to replace the lookup table with a generalizing function approximator such as a neural net. Although this has been successful in the domain of backgammon, there is no guarantee of convergence. In this paper, we show that the combination of dynamic programming and function approximation is not robust, and in even very benign cases, may produce an entirely wrong policy. We then introduce Grow-Support, a new algorithm which is safe from divergence yet can still reap the benefits of successful generalization. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1152277. 	[Title] Misclassification Minimization [Abstract] The problem of minimizing the number of misclassified points by a plane, attempting to separate two point sets with intersecting convex hulls in n-dimensional real space, is formulated as a linear program with equilibrium constraints (LPEC). This general LPEC can be converted to an exact penalty problem with a quadratic objective and linear constraints. A Frank-Wolfe-type algorithm is proposed for the penalty problem that terminates at a stationary point or a global solution. Novel aspects of the approach include: (i) A linear complementarity formulation of the step function that "counts" misclassifications, (ii) Exact penalty formulation without boundedness, nondegeneracy or constraint qualification assumptions, (iii) An exact solution extraction from the sequence of minimizers of the penalty function for a finite value of the penalty parameter for the general LPEC and an explicitly exact solution for the LPEC with uncoupled constraints, and (iv) A parametric quadratic programming formulation of the LPEC associated with the misclassification minimization problem. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #59798. 	[Title] PATTERN RECOGNITION VIA LINEAR PROGRAMMING THEORY AND APPLICATION TO MEDICAL DIAGNOSIS [Abstract] A decision problem associated with a fundamental nonconvex model for linearly inseparable pattern sets is shown to be NP-complete. Another nonconvex model that employs an 1 norm instead of the 2-norm, can be solved in polynomial time by solving 2n linear programs, where n is the (usually small) dimensionality of the pattern space. An effective LP-based finite algorithm is proposed for solving the latter model. The algorithm is employed to obtain a noncon-vex piecewise-linear function for separating points representing measurements made on fine needle aspirates taken from benign and malignant human breasts. A computer program trained on 369 samples has correctly diagnosed each of 45 new samples encountered and is currently in use at the University of Wisconsin Hospitals. 1. Introduction. The fundamental problem we wish to address is that of Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #350362. 	[Title] Improving Regressors using Boosting Techniques [Abstract] In the regression context, boosting and bagging are techniques to build a committee of regressors that may be superior to a single regressor. We use regression trees as fundamental building blocks in bagging committee machines and boosting committee machines. Performance is analyzed on three non-linear functions and the Boston housing database. In all cases, boosting is at least equivalent, and in most cases better than bagging in terms of prediction error. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #3229. 	[Title] Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2 [Abstract] Neural computation, also called connectionism, parallel distributed processing, neural network modeling or brain-style computation, has grown rapidly in the last decade. Despite this explosion, and ultimately because of impressive applications, there has been a dire need for a concise introduction from a theoretical perspective, analyzing the strengths and weaknesses of connectionist approaches and establishing links to other disciplines, such as statistics or control theory. The Introduction to the Theory of Neural Computation by Hertz, Krogh and Palmer (subsequently referred to as HKP) is written from the perspective of physics, the home discipline of the authors. The book fulfills its mission as an introduction for neural network novices, provided that they have some background in calculus, linear algebra, and statistics. It covers a number of models that are often viewed as disjoint. Critical analyses and fruitful comparisons between these models Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #8224. 	[Title] Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine [Abstract] Ourston and Mooney, 1990b ] D. Ourston and R. J. Mooney. Improving shared rules in multiple category domain theories. Technical Report AI90-150, Artificial Intelligence Labora tory, University of Texas, Austin, TX, December 1990. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1152673. 	[Title] Misclassification Minimization [Abstract] The problem of minimizing the number of misclassified points by a plane, attempting to separate two point sets with intersecting convex hulls in n-dimensional real space, is formulated as a linear program with equilibrium constraints (LPEC). This general LPEC can be converted to an exact penalty problem with a quadratic objective and linear constraints. A Frank-Wolfe-type algorithm is proposed for the penalty problem that terminates at a stationary point or a global solution. Novel aspects of the approach include: (i) A linear complementarity formulation of the step function that "counts" misclassifications, (ii) Exact penalty formulation without boundedness, nondegeneracy or constraint qualification assumptions, (iii) An exact solution extraction from the sequence of minimizers of the penalty function for a finite value of the penalty parameter for the general LPEC and an explicitly exact solution for the LPEC with uncoupled constraints, and (iv) A parametric quadratic programming formulation of the LPEC associated with the misclassification minimization problem. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #854434. 	[Title] Predicting Lifetimes in Dynamically Allocated Memory [Abstract] Predictions of lifetimes of dynamically allocated objects can be used to improve time and space efficiency of dynamic memory management in computer programs. Barrett and Zorn [1993] used a simple lifetime predictor and demonstrated this improvement on a variety of computer programs. In this paper, we use decision trees to do lifetime prediction on the same programs and show significantly better prediction. Our method also has the advantage that during training we can use a large number of features and let the decision tree automatically choose the relevant subset. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1110947. 	[Title] Lazy Induction Triggered by CBR [Abstract] In recent years, case-based reasoning has been demonstrated to be highly useful for problem solving in complex domains. Also, mixed paradigm approaches emerged for combining CBR and induction techniques aiming at verifying the knowledge and/or building an efficient case memory. However, in complex domains induction over the whole problem space is often not possible or too time consuming. In this paper, an approach is presented which (owing to a close interaction with the CBR part) attempts to induce rules only for a particular context, i.e. for a problem just being solved by a CBR-oriented system. These rules may then be used for indexing purposes or similarity assessment in order to support the CBR process in the future. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #27174. 	[Title] Mathematical Programming in Neural Networks [Abstract] This paper highlights the role of mathematical programming, particularly linear programming, in training neural networks. A neural network description is given in terms of separating planes in the input space that suggests the use of linear programming for determining these planes. A more standard description in terms of a mean square error in the output space is also given, which leads to the use of unconstrained minimization techniques for training a neural network. The linear programming approach is demonstrated by a brief description of a system for breast cancer diagnosis that has been in use for the last four years at a major medical facility. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #144679. 	[Title] Learning When Reformulation is Appropriate for Iterative Design [Abstract] It is well known that search-space reformulation can improve the speed and reliability of numerical optimization in engineering design. We argue that the best choice of reformulation depends on the design goal, and present a technique for automatically constructing rules that map the design goal into a reformulation chosen from a space of possible reformulations. We tested our technique in the domain of racing-yacht-hull design, where each reformulation corresponds to incorporating constraints into the search space. We applied a standard inductive-learning algorithm, C4.5, to a set of training data describing which constraints are active in the optimal design for each goal encountered in a previous design session. We then used these rules to choose an appropriate reformulation for each of a set of test cases. Our experimental results show that using these reformulations improves both the speed and the reliability of design optimization, outperforming competing methods and approaching the best performance possible. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #36140. 	[Title] Error-Correcting Output Codes: A General Method for Improving Multiclass Inductive Learning Programs [Abstract] Multiclass learning problems involve finding a definition for an unknown function f(x) whose range is a discrete set containing k &gt; 2 values (i.e., k "classes"). The definition is acquired by studying large collections of training examples of the form hx i ; f(x i )i. Existing approaches to this problem include (a) direct application of multiclass algorithms such as the decision-tree algorithms ID3 and CART, (b) application of binary concept learning algorithms to learn individual binary functions for each of the k classes, and (c) application of binary concept learning algorithms with distributed output codes such as those employed by Sejnowski and Rosenberg in the NETtalk system. This paper compares these three approaches to a new technique in which BCH error-correcting codes are employed as a distributed output representation. We show that these output representations improve the performance of ID3 on the NETtalk task and of backpropagation on an isolated-letter speech-recognition task. These results demonstrate that error-correcting output codes provide a general-purpose method for improving the performance of inductive learning programs on multiclass problems. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #10796. 	[Title] An Improved Algorithm for Incremental Induction of Decision Trees [Abstract] Technical Report 94-07 February 7, 1994 (updated April 25, 1994) This paper will appear in Proceedings of the Eleventh International Conference on Machine Learning. Abstract This paper presents an algorithm for incremental induction of decision trees that is able to handle both numeric and symbolic variables. In order to handle numeric variables, a new tree revision operator called `slewing' is introduced. Finally, a non-incremental method is given for finding a decision tree based on a direct metric of a candidate tree. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #30895. 	[Title] Induction of Oblique Decision Trees [Abstract] This paper introduces a randomized technique for partitioning examples using oblique hyperplanes. Standard decision tree techniques, such as ID3 and its descendants, partition a set of points with axis-parallel hyper-planes. Our method, by contrast, attempts to find hyperplanes at any orientation. The purpose of this more general technique is to find smaller but equally accurate decision trees than those created by other methods. We have tested our algorithm on both real and simulated data, and found that in some cases it produces surprisingly small trees without losing predictive accuracy. Small trees allow us, in turn, to obtain simple qualitative descriptions of each problem domain. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #368431. 	[Title] Decision Tree Induction: How Effective is the Greedy Heuristic? [Abstract] Most existing decision tree systems use a greedy approach to induce trees | locally optimal splits are induced at every node of the tree. Although the greedy approach is suboptimal, it is believed to produce reasonably good trees. In the current work, we attempt to verify this belief. We quantify the goodness of greedy tree induction empirically, using the popular decision tree algorithms, C4.5 and CART. We induce decision trees on thousands of synthetic data sets and compare them to the corresponding optimal trees, which in turn are found using a novel map coloring idea. We measure the effect on greedy induction of variables such as the underlying concept complexity, training set size, noise and dimensionality. Our experiments show, among other things, that the expected classification cost of a greedily induced tree is consistently very close to that of the optimal tree. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #36145. 	[Title] Generating Accurate and Diverse Members of a Neural-Network Ensemble [Abstract] Neural-network ensembles have been shown to be very accurate classification techniques. Previous work has shown that an effective ensemble should consist of networks that are not only highly correct, but ones that make their errors on different parts of the input space as well. Most existing techniques, however, only indirectly address the problem of creating such a set of networks. In this paper we present a technique called Addemup that uses genetic algorithms to directly search for an accurate and diverse set of trained networks. Addemup works by first creating an initial population, then uses genetic operators to continually create new networks, keeping the set of networks that are as accurate as possible while disagreeing with each other as much as possible. Experiments on three DNA problems show that Addemup is able to generate a set of trained networks that is more accurate than several existing approaches. Experiments also show that Addemup is able to effectively incorporate prior knowledge, if available, to improve the quality of its ensemble. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #30901. 	[Title] Lookahead and Pathology in Decision Tree Induction [Abstract] The standard approach to decision tree induction is a top-down, greedy algorithm that makes locally optimal, irrevocable decisions at each node of a tree. In this paper, we empirically study an alternative approach, in which the algorithms use one-level lookahead to decide what test to use at a node. We systematically compare, using a very large number of real and artificial data sets, the quality of decision trees induced by the greedy approach to that of trees induced using lookahead. The main observations from our experiments are: (i) the greedy approach consistently produced trees that were just as accurate as trees produced with the much more expensive lookahead step; and (ii) we observed many instances of pathology, i.e., lookahead produced trees that were both larger and less accurate than trees produced without it. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #31927. 	[Title] A Generalized Hidden Markov Model for the Recognition of Human Genes in DNA [Abstract] We present a statistical model of genes in DNA. A Generalized Hidden Markov Model (GHMM) provides the framework for describing the grammar of a legal parse of a DNA sequence (Stormo & Haussler 1994). Probabilities are assigned to transitions between states in the GHMM and to the generation of each nucleotide base given a particular state. Machine learning techniques are applied to optimize these probabilities using a standardized training set. Given a new candidate sequence, the best parse is deduced from the model using a dynamic programming algorithm to identify the path through the model with maximum probability. The GHMM is flexible and modular, so new sensors and additional states can be inserted easily. In addition, it provides simple solutions for integrating cardinality constraints, reading frame constraints, "indels", and homology searching. The description and results of an implementation of such a gene-finding model, called Genie, is presented. The exon sensor is a codon frequency model conditioned on windowed nucleotide frequency and the preceding codon. Two neural networks are used, as in (Brunak, Engelbrecht, & Knudsen 1991), for splice site prediction. We show that this simple model performs quite well. For a cross-validated standard test set of 304 genes [ftp://www-hgc.lbl.gov/pub/genesets] in human DNA, our gene-finding system identified up to 85% of protein-coding bases correctly with a specificity of 80%. 58% of exons were exactly identified with a specificity of 51%. Genie is shown to perform favorably compared with several other gene-finding systems. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1104055. 	[Title] LEARNING BY USING DYNAMIC FEATURE COMBINATION AND SELECTION [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #194617. 	[Title] Finding Genes in DNA with a Hidden Markov Model [Abstract] This study describes a new Hidden Markov Model (HMM) system for segmenting uncharacterized genomic DNA sequences into exons, introns, and intergenic regions. Separate HMM modules were designed and trained for specific regions of DNA: exons, introns, intergenic regions, and splice sites. The models were then tied together to form a biologically feasible topology. The integrated HMM was trained further on a set of eukaryotic DNA sequences, and tested by using it to segment a separate set of sequences. The resulting HMM system, which is called VEIL (Viterbi Exon-Intron Locator), obtains an overall accuracy on test data of 92% of total bases correctly labelled, with a correlation coefficient of 0.73. Using the more stringent test of exact exon prediction, VEIL correctly located both ends of 53% of the coding exons, and 49% of the exons it predicts are exactly correct. These results compare favorably to the best previous results for gene structure prediction, and demonstrate the benefits of using HMMs for this problem. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #31932. 	[Title] A Decision Tree System for Finding Genes in DNA [Abstract] MORGAN is an integrated system for finding genes in vertebrate DNA sequences. MORGAN uses a variety of techniques to accomplish this task, the most distinctive of which is a decision tree classifier. The decision tree system is combined with new methods for identifying start codons, donor sites, and acceptor sites, and these are brought together in a frame-sensitive dynamic programming algorithm that finds the optimal segmentation of a DNA sequence into coding and noncoding regions (exons and introns). The optimal segmentation is dependent on a separate scoring function that takes a subsequence and assigns to it a score reflecting the probability that the sequence is an exon. The scoring functions in MORGAN are sets of decision trees that are combined to give a probability estimate. Experimental results on a database of 570 vertebrate DNA sequences show that MORGAN has excellent performance by many different measures. On a separate test set, it achieves an overall accuracy of 95%, with a correlation coefficient of 0.78 and a sensitivity and specificity for coding bases of 83% and 79%. In addition, MORGAN identifies 58% of coding exons exactly; i.e., both the beginning and end of the coding regions are predicted correctly. This paper describes the MORGAN system, including its decision tree routines and the algorithms for site recognition, and its performance on a benchmark database of vertebrate DNA. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #10183. 	[Title] Concept Learning and Heuristic Classification in Weak-Theory Domains 1 [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6216. 	[Title] Generalization in Reinforcement Learning: Successful Examples Using Sparse Coarse Coding [Abstract] On large problems, reinforcement learning systems must use parameterized function approximators such as neural networks in order to generalize between similar situations and actions. In these cases there are no strong theoretical results on the accuracy of convergence, and computational results have been mixed. In particular, Boyan and Moore reported at last year's meeting a series of negative results in attempting to apply dynamic programming together with function approximation to simple control problems with continuous state spaces. In this paper, we present positive results for all the control tasks they attempted, and for one that is significantly larger. The most important differences are that we used sparse-coarse-coded function approximators (CMACs) whereas they used mostly global function approximators, and that we learned online whereas they learned o*ine. Boyan and Moore and others have suggested that the problems they encountered could be solved by using actual outcomes ("rollouts"), as in classical Monte Carlo methods, and as in the TD() algorithm when = 1. However, in our experiments this always resulted in substantially poorer performance. We conclude that reinforcement learning can work robustly in conjunction with function approximators, and that there is little justification at present for avoiding the case of general . Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1154251. 	[Title] Misclassification Minimization [Abstract] The problem of minimizing the number of misclassified points by a plane, attempting to separate two point sets with intersecting convex hulls in n-dimensional real space, is formulated as a linear program with equilibrium constraints (LPEC). This general LPEC can be converted to an exact penalty problem with a quadratic objective and linear constraints. A Frank-Wolfe-type algorithm is proposed for the penalty problem that terminates at a stationary point or a global solution. Novel aspects of the approach include: (i) A linear complementarity formulation of the step function that "counts" misclassifications, (ii) Exact penalty formulation without boundedness, nondegeneracy or constraint qualification assumptions, (iii) An exact solution extraction from the sequence of minimizers of the penalty function for a finite value of the penalty parameter for the general LPEC and an explicitly exact solution for the LPEC with uncoupled constraints, and (iv) A parametric quadratic programming formulation of the LPEC associated with the misclassification minimization problem. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #11339. 	[Title] Exploring the Decision Forest: An Empirical Investigation of Occam's Razor in Decision Tree Induction [Abstract] We report on a series of experiments in which all decision trees consistent with the training data are constructed. These experiments were run to gain an understanding of the properties of the set of consistent decision trees and the factors that affect the accuracy of individual trees. In particular, we investigated the relationship between the size of a decision tree consistent with some training data and the accuracy of the tree on test data. The experiments were performed on a massively parallel Maspar computer. The results of the experiments on several artificial and two real world problems indicate that, for many of the problems investigated, smaller consistent decision trees are on average less accurate than the average accuracy of slightly larger trees. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #96335. 	[Title] Geometry in Learning [Abstract] One of the fundamental problems in learning is identifying members of two different classes. For example, to diagnose cancer, one must learn to discriminate between benign and malignant tumors. Through examination of tumors with previously determined diagnosis, one learns some function for distinguishing the benign and malignant tumors. Then the acquired knowledge is used to diagnose new tumors. The perceptron is a simple biologically inspired model for this two-class learning problem. The perceptron is trained or constructed using examples from the two classes. Then the perceptron is used to classify new examples. We describe geometrically what a perceptron is capable of learning. Using duality, we develop a framework for investigating different methods of training a perceptron. Depending on how we define the "best" perceptron, different minimization problems are developed for training the perceptron. The effectiveness of these methods is evaluated empirically on four practical applications: breast cancer diagnosis, detection of heart disease, political voting habits, and sonar recognition. This paper does not assume prior knowledge of machine learning or pattern recognition. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #708945. 	[Title] Massive Data Discrimination via Linear Support Vector Machines [Abstract] A linear support vector machine formulation is used to generate a fast, finitely-terminating linear-programming algorithm for discriminating between two massive sets in n-dimensional space, where the number of points can be orders of magnitude larger than n. The algorithm creates a succession of sufficiently small linear programs that separate chunks of the data at a time. The key idea is that a small number of support vectors, corresponding to linear programming constraints with positive dual variables, are carried over between the successive small linear programs, each of which containing a chunk of the data. We prove that this procedure is monotonic and terminates in a finite number of steps at an exact solution that leads to a globally optimal separating plane for the entire dataset. Numerical results on fully dense publicly available datasets, numbering 20,000 to 1 million points in 32-dimensional space, confirm the theoretical results and demonstrate the ability to handle very large problems. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #174418. 	[Title] Improving the Accuracy and Speed of Support Vector Machines [Abstract] Support Vector Learning Machines (SVM) are finding application in pattern recognition, regression estimation, and operator inversion for ill-posed problems. Against this very general backdrop, any methods for improving the generalization performance, or for improving the speed in test phase, of SVMs are of increasing interest. In this paper we combine two such techniques on a pattern recognition problem. The method for improving generalization performance (the "virtual support vector" method) does so by incorporating known invariances of the problem. This method achieves a drop in the error rate on 10,000 NIST test digit images of 1.4% to 1.0%. The method for improving the speed (the "reduced set" method) does so by approximating the support vector decision surface. We apply this method to achieve a factor of fifty speedup in test phase over the virtual support vector machine. The combined approach yields a machine which is both 22 times faster than the original machine, and which has better generalization performance, achieving 1.1% error. The virtual support vector method is applicable to any SVM problem with known invariances. The reduced set method is applicable to any support vector machine. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #22875. 	[Title] Inductive Learning and Case-Based Reasoning [Abstract] This paper describes an application of an inductive learning techniques to case-based reasoning. We introduce two main forms of induction, define case-based reasoning and present a combination of both. The evaluation of the proposed system, called TA3, is carried out on a classification task, namely character recognition. We show how inductive knowledge improves knowledge representation and in turn flexibility of the system, its performance (in terms of classification accuracy) and its scalability. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #2653. 	[Title] A decision-theoretic generalization of on-line learning and an application to boosting how the weight-update rule [Abstract] We consider the problem of dynamically apportioning resources among a set of options in a worst-case on-line framework. The model we study can be interpreted as a broad, abstract extension of the well-studied on-line prediction model to a general decision-theoretic setting. We show that the multiplicative weight-update rule of Littlestone and Warmuth [10] can be adapted to this model yielding bounds that are slightly weaker in some cases, but applicable to a considerably more general class of learning problems. We show how the resulting learning algorithm can be applied to a variety of problems, including gambling, multiple-outcome prediction, repeated games and prediction of points in R n Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #25181. 	[Title] Bilinear Separation of Two Sets in n-Space [Abstract] The NP-complete problem of determining whether two disjoint point sets in the n-dimensional real space R n can be separated by two planes is cast as a bilinear program, that is minimizing the scalar product of two linear functions on a polyhedral set. The bilinear program, which has a vertex solution, is processed by an iterative linear programming algorithm that terminates in a finite number of steps at a point satisfying a necessary optimality condition or at a global minimum. Encouraging computational experience on a number of test problems is reported. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #4584. 	[Title] References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages [Abstract] Miller, G. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. The Psychological Review, 63(2):81-97. Schmidhuber, J. (1990b). Towards compositional learning with dynamic neural networks. Technical Report FKI-129-90, Technische Universitat Munchen, Institut fu Informatik. Servan-Schreiber, D., Cleermans, A., and McClelland, J. (1988). Encoding sequential structure in simple recurrent networks. Technical Report CMU-CS-88-183, Carnegie Mellon University, Computer Science Department. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #215912. 	[Title] A Method for Identifying Splice Sites and Translational Start Sites in [Abstract] This paper describes a new method for determining the consensus sequences that signal the start of translation and the boundaries between exons and introns (donor and acceptor sites) in eukaryotic mRNA. The method takes into account the dependencies between adjacent bases, in contrast to the usual technique of considering each position independently. When coupled with a dynamic program to compute the most likely sequence, new consensus sequences emerge. The consensus sequence information is summarized in conditional probability matrices which, when used to locate signals in uncharacter-ized genomic DNA, have greater sensitivity and specificity than conventional matrices. Species-specific versions of these matrices are especially effective at distinguishing true and false sites. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #285675. 	[Title] Parsimonious Least Norm Approximation [Abstract] A theoretically justifiable fast finite successive linear approximation algorithm is proposed for obtaining a parsimonious solution to a corrupted linear system Ax = b + p, where the corruption p is due to noise or error in measurement. The proposed linear-programming-based algorithm finds a solution x by parametrically minimizing the number of nonzero elements in x and the error k Ax b p k 1 . Numerical tests on a signal-processing-based example indicate that the proposed method is comparable to a method that parametrically minimizes the 1-norm of the solution x and the error k Ax b p k 1 , and that both methods are superior, by orders of magnitude, to solutions obtained by least squares as well by combinatorially choosing an optimal solution with a specific number of nonzero elements. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #27249. 	[Title] Machine Learning Bias, Statistical Bias, and Statistical Variance of Decision Tree Algorithms [Abstract] The term "bias" is widely used|and with different meanings|in the fields of machine learning and statistics. This paper clarifies the uses of this term and shows how to measure and visualize the statistical bias and variance of learning algorithms. Statistical bias and variance can be applied to diagnose problems with machine learning bias, and the paper shows four examples of this. Finally, the paper discusses methods of reducing bias and variance. Methods based on voting can reduce variance, and the paper compares Breiman's bagging method and our own tree randomization method for voting decision trees. Both methods uniformly improve performance on data sets from the Irvine repository. Tree randomization yields perfect performance on the Letter Recognition task. A weighted nearest neighbor algorithm based on the infinite bootstrap is also introduced. In general, decision tree algorithms have moderate-to-high variance, so an important implication of this work is that variance|rather than appropriate or inappropriate machine learning bias|is an important cause of poor performance for decision tree algorithms. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1272. 	[Title] Learning a set of primitive actions with an Induction of decision trees. Machine Learning, 1(1):81-106, [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1103610. 	[Title] A hierarchical ensemble of decision trees applied to classifying data from a psychological experiment [Abstract] Classifying by hand complex data coming from psychology experiments can be a long and difficult task, because of the quantity of data to classify and the amount of training it may require. One way to alleviate this problem is to use machine learning techniques. We built a classifier based on decision trees that reproduces the classifying process used by two humans on a sample of data and that learns how to classify unseen data. The automatic classifier proved to be more accurate, more constant and much faster than classification by hand. Please summarise all the given information.

Paper #3243 has the title `A System for Induction of Oblique Decision Trees  
` and the abstract `This article describes a new system for induction of oblique decision trees. This system, OC1, combines deterministic hill-climbing with two forms of randomization to find a good oblique split (in the form of a hyperplane) at each node of a decision tree. Oblique decision tree methods are tuned especially for domains in which the attributes are numeric, although they can be adapted to symbolic or mixed symbolic/numeric attributes. We present extensive empirical studies, using both real and artificial data, that analyze OC1's ability to construct oblique trees that are smaller and more accurate than their axis-parallel counterparts. We also examine the benefits of randomization for the construction of oblique decision trees.
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Question: Which category should Paper #3243 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #3243 has the title `A System for Induction of Oblique Decision Trees  
` and the abstract `This article describes a new system for induction of oblique decision trees. This system, OC1, combines deterministic hill-climbing with two forms of randomization to find a good oblique split (in the form of a hyperplane) at each node of a decision tree. Oblique decision tree methods are tuned especially for domains in which the attributes are numeric, although they can be adapted to symbolic or mixed symbolic/numeric attributes. We present extensive empirical studies, using both real and artificial data, that analyze OC1's ability to construct oblique trees that are smaller and more accurate than their axis-parallel counterparts. We also examine the benefits of randomization for the construction of oblique decision trees.
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Question: Which category should Paper #3243 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Theory