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 #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 #93320. 	[Title] Some Experiments with a Hybrid Model for Learning Sequential Decision Making [Abstract]  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 #180373. 	[Title] CANCER DIAGNOSIS AND PROGNOSIS VIA LINEAR-PROGRAMMING-BASED MACHINE LEARNING [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #54550. 	[Title] Analysis and Empirical Studies of Derivational Analogy [Abstract] Derivational analogy is a technique for reusing problem solving experience to improve problem solving performance. This research addresses an issue common to all problem solvers that use derivational analogy: overcoming the mismatches between past experiences and new problems that impede reuse. First, this research describes the variety of mismatches that can arise and proposes a new approach to derivational analogy that uses appropriate adaptation strategies for each. Second, it compares this approach with seven others in a common domain. This empirical study shows that derivational analogy is almost always more efficient than problem solving from scratch, but the amount it contributes depends on its ability to overcome mismatches 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 #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 #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 #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 #1108267. 	[Title] Cooperative Bayesian and Case-Based Reasoning for Solving Multiagent Planning Tasks [Abstract] We describe an integrated problem solving architecture named INBANCA in which Bayesian networks and case-based reasoning (CBR) work cooperatively on multiagent planning tasks. This includes two-team dynamic tasks, and this paper concentrates on simulated soccer as an example. Bayesian networks are used to characterize action selection whereas a case-based approach is used to determine how to implement actions. This paper has two contributions. First, we survey integrations of case-based and Bayesian approaches from the perspective of a popular CBR task decomposition framework, thus explaining what types of integrations have been attempted. This allows us to explain the unique aspects of our proposed integration. Second, we demonstrate how Bayesian nets can be used to provide environmental context, and thus feature selection information, for the case-based reasoner. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #10798. 	[Title] Learning physical descriptions from functional definitions, examples, Learning from examples: The effect of different conceptual [Abstract]  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 #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 #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 #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 #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 #259772. 	[Title] Reasoning with Portions of Precedents [Abstract] This paper argues that the task of matching in case-based reasoning can often be improved by comparing new cases to portions of precedents. An example is presented that illustrates how combining portions of multiple precedents can permit new cases to be resolved that would be indeterminate if new cases could only be compared to entire precedents. A system that uses of portions of precedents for legal analysis in the domain of Texas worker's compensation law, GREBE, is described, and examples of GREBE's analysis that combine reasoning steps from multiple precedents are presented. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #10430. 	[Title] Rules and Precedents as Complementary Warrants Complementarity of Rules and Precedents for Classification In a [Abstract] This paper describes a model of the complementarity of rules and precedents in the classification task. Under this model, precedents assist rule-based reasoning by operationalizing abstract rule antecedents. Conversely, rules assist case-based reasoning through case elaboration, the process of inferring case facts in order to increase the similarity between cases, and term reformulation, the process of replacing a term whose precedents only weakly match a case with terms whose precedents strongly match the case. Fully exploiting this complementarity requires a control strategy characterized by impartiality, the absence of arbitrary ordering restrictions on the use of rules and precedents. An impartial control strategy was implemented in GREBE in the domain of Texas worker's compensation law. In a preliminary evaluation, GREBE's performance was found to be as good or slightly better than the performance of law students on the same task. A case is classified as belonging to a particular category by relating its description to the criteria for category membership. The justifications, or warrants [Toulmin, 1958], that can relate a case to a category, can vary widely in the generality of their antecedents. For example, consider warrants for classifying a case into the legal category "negligence." A rule, such as "An action is negligent if the actor fails to use reasonable care and the failure is the proximate cause of an injury," has very general antecedent terms (e.g., "breach of reasonable care"). Conversely, a precedent, such as "Dr. Jones was negligent because he failed to count sponges during surgery and as a result left a sponge in Smith," has very specific antecedent terms (e.g., "failure to count sponges"). Both types of warrants have been used by classification systems to relate cases to categories. Classification systems have used precedents to help match the antecedents of rules with cases. Completing this match is difficult when the terms in the antecedent are open-textured, i.e., when there is significant uncertainty whether they match specific facts [Gardner, 1984, McCarty and Sridharan, 1982]. This problem results from the "generality gap" separating abstract terms from specific facts [Porter et al., 1990]. Precedents of an open-textured term, i.e., past cases to which the term applied, can be used to bridge this gap. Unlike rule antecedents, the antecedents of precedents are at the same level of generality as cases, so no generality gap exists between precedents and new cases. Precedents therefore reduce the problem of matching specific case facts with open-textured terms to the problem of matching two sets of specific facts. For example, an injured employee's entitlement to worker's compensation depends on whether he was injured during an activity "in furtherance of employment." Determining whether any particular case should be classified as a compensable injury therefore requires matching the specific facts of the case (e.g., John was injured in an automobile accident while driving to his office) to the open-textured term "activity in furtherance of employment." The gap in generality between the case description and the abstract term makes this match problematical. However, completing this match may be much easier if there are precedents of the term "activity in furtherance of employment" (e.g., Mary's injury was not compensable because it occurred while she was driving to work, which is not an activity in furtherance of employment; Bill's injury was compensable because it occurred while he was driving to a house to deliver a pizza, an activity in furtherance of employment). In this case, John's driving to his office closely matches Mary's driving to work, so Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6213. 	[Title] Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction, [Abstract] This article introduces a class of incremental learning procedures specialized for prediction|that is, for using past experience with an incompletely known system to predict its future behavior. Whereas conventional prediction-learning methods assign credit by means of the difference between predicted and actual outcomes, the new methods assign credit by means of the difference between temporally successive predictions. Although such temporal-difference methods have been used in Samuel's checker player, Holland's bucket brigade, and the author's Adaptive Heuristic Critic, they have remained poorly understood. Here we prove their convergence and optimality for special cases and relate them to supervised-learning methods. For most real-world prediction problems, temporal-difference methods require less memory and less peak computation than conventional methods; and they produce more accurate predictions. We argue that most problems to which supervised learning is currently applied are really prediction problems of the sort to which temporal-difference methods can be applied to advantage. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #417017. 	[Title] Relating Relational Learning Algorithms [Abstract] Relational learning algorithms are of special interest to members of the machine learning community; they offer practical methods for extending the representations used in algorithms that solve supervised learning tasks. Five approaches are currently being explored to address issues involved with using relational representations. This paper surveys algorithms embodying these approaches, summarizes their empirical evaluations, highlights their commonalities, and suggests potential directions for future research. 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 #46536. 	[Title] Linear Machine Decision Trees [Abstract] COINS Technical Report 91-10 January 1991 Abstract This article presents an algorithm for inducing multiclass decision trees with multivariate tests at internal decision nodes. Each test is constructed by training a linear machine and eliminating variables in a controlled manner. Empirical results demonstrate that the algorithm builds small accurate trees across a variety of tasks. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1120713. 	[Title] A Computational Model of Ratio Decidendi [Abstract] This paper proposes a model of ratio decidendi as a justification structure consisting of a series of reasoning steps, some of which relate abstract predicates to other abstract predicates and some of which relate abstract predicates to specific facts. This model satisfies an important set of characteristics of ratio decidendi identified from the jurisprudential literature. In particular, the model shows how the theory under which a case is decided controls its precedential effect. By contrast, a purely exemplar-based model of ratio decidendi fails to account for the dependency of prece-dential effect on the theory of decision. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #10186. 	[Title] Learning Default Concepts [Abstract] Classical concepts, based on necessary and sufficient defining conditions, cannot classify logically insufficient object descriptions. Many reasoning systems avoid this limitation by using "default concepts" to classify incompletely described objects. We address the task of learning such default concepts from observational data. We model the underlying performance task | classifying incomplete examples | by a probabilistic process where random test examples are passed through a "blocker" that can hide object attributes from the classifier. We then address the task of learning accurate default concepts from random training examples. We survey the learning techniques that have been proposed for this task in the machine learning and knowledge representation literatures, investigate the relative merits of each, and show that a superior learning technique can be developed from well known statistical principles. Finally, we extend Valiant's pac-learning framework to this context and obtain a number of useful learnability results. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6217. 	[Title] Online Learning with Random Representations [Abstract] We consider the requirements of online learning|learning which must be done incrementally and in realtime, with the results of learning available soon after each new example is acquired. Despite the abundance of methods for learning from examples, there are few that can be used effectively for online learning, e.g., as components of reinforcement learning systems. Most of these few, including radial basis functions, CMACs, Ko-honen's self-organizing maps, and those developed in this paper, share the same structure. All expand the original input representation into a higher dimensional representation in an unsupervised way, and then map that representation to the final answer using a relatively simple supervised learner, such as a perceptron or LMS rule. Such structures learn very rapidly and reliably, but have been thought either to scale poorly or to require extensive domain knowledge. To the contrary, some researchers (Rosenblatt, 1962; Gallant & Smith, 1987; Kanerva, 1988; Prager & Fallside, 1988) have argued that the expanded representation can be chosen largely at random with good results. The main contribution of this paper is to develop and test this hypothesis. We show that simple random-representation methods can perform as well as nearest-neighbor methods (while being more suited to online learning), and significantly better than backpropagation. We find that the size of the random representation does increase with the dimensionality of the problem, but not unreasonably so, and that the required size can be reduced substantially using unsupervised-learning techniques. Our results suggest that randomness has a useful role to play in online supervised learning and constructive induction. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #20942. 	[Title] Decision Tree Induction Based on Efficient Tree Restructuring [Abstract] The ability to restructure a decision tree efficiently enables a variety of approaches to decision tree induction that would otherwise be prohibitively expensive. Two such approaches are described here, one being incremental tree induction (ITI), and the other being non-incremental tree induction using a measure of tree quality instead of test quality (DMTI). These approaches and several variants offer new computational and classifier characteristics that lend themselves to particular applications. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1113934. 	[Title] Integration of Case-Based Reasoning and Neural Networks Approaches for Classification [Abstract] Several different approaches have been used to describe concepts for supervised learning tasks. In this paper we describe two approaches which are: prototype-based incremental neural networks and case-based reasoning approaches. We show then how we can improve a prototype-based neural network model by storing some specific instances in a CBR memory system. This leads us to propose a co-processing hybrid model for classification. 1 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 #47570. 	[Title] Justification Structures for Document Reuse [Abstract] Document drafting|an important problem-solving task of professionals in a wide variety of fields|typifies a design task requiring complex adaptation for case reuse. This paper proposes a framework for document reuse based on an explicit representation of the illocutionary and rhetorical structure underlying documents. Explicit representation of this structure facilitates (1) interpretation of previous documents by enabling them to "explain themselves," (2) construction of documents by enabling document drafters to issue goal-based specifications and rapidly retrieve documents with similar intentional structure, and (3) mainte nance of multi-generation documents. 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 #22876. 	[Title] A Case-Based Reasoning Approach [Abstract] The AAAI Fall Symposium; Flexible Computation in Intelligent Systems: Results, Issues, and Opportunities. Nov. 9-11, 1996, Cambridge, MA Abstract This paper presents a case-based reasoning system TA3. We address the flexibility of the case-based reasoning process, namely flexible retrieval of relevant experiences, by using a novel similarity assessment theory. To exemplify the advantages of such an approach, we have experimentally evaluated the system and compared its performance to the performance of non-flexible version of TA3 and to other machine learning algorithms on several domains. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #601567. 	[Title] Geometric Comparison of Classifications and Rule Sets* [Abstract] We present a technique for evaluating classifications by geometric comparison of rule sets. Rules are represented as objects in an n-dimensional hyperspace. The similarity of classes is computed from the overlap of the geometric class descriptions. The system produces a correlation matrix that indicates the degree of similarity between each pair of classes. The technique can be applied to classifications generated by different algorithms, with different numbers of classes and different attribute sets. Experimental results from a case study in a medical domain are included. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1114336. 	[Title] Four Challenges for a Computational Model of Legal Precedent [Abstract] Identifying the open research issues in a field is a necessary step for progress in that field. This paper describes four open research problems in computational models of precedent-based legal reasoning: relating case representation to precedent use; modeling the selection and construction of both arguments based on pairwise case comparison and multiple-precedent arguments; modeling the process whereby purposes, policies, and principles are used in case similarity assessment; and extending the applicability of precedents to tasks other than classification. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6378. 	[Title] Learning to coordinate without sharing information [Abstract] Researchers in the field of Distributed Artificial Intelligence (DAI) have been developing efficient mechanisms to coordinate the activities of multiple autonomous agents. The need for coordination arises because agents have to share resources and expertise required to achieve their goals. Previous work in the area includes using sophisticated information exchange protocols, investigating heuristics for negotiation, and developing formal models of possibilities of conflict and cooperation among agent interests. In order to handle the changing requirements of continuous and dynamic environments, we propose learning as a means to provide additional possibilities for effective coordination. We use reinforcement learning techniques on a block pushing problem to show that agents can learn complimentary policies to follow a desired path without any knowledge about each other. We theoretically analyze and experimentally verify the effects of learning rate on system convergence, and demonstrate benefits of using learned coordination knowledge on similar problems. Reinforcement learning based coordination can be achieved in both cooperative and non-cooperative domains, and in domains with noisy communication channels and other stochastic characteristics that present a formidable challenge to using other coordination schemes. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #887. 	[Title] Some studies in machine learning using the game of checkers. IBM Journal, 3(3):211-229, 1959. Some [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #31097. 	[Title] What Daimler-Benz has learned as an industrial partner from the Machine Learning Project StatLog [Abstract] Author of this paper was co-ordinator of the Machine Learning project StatLog during 1990-1993. This project was supported financially by the European Community. The main aim of StatLog was to evaluate different learning algorithms using real industrial and commercial applications. As an industrial partner and contributor, Daimler-Benz has introduced different applications to Stat-Log among them fault diagnosis, letter and digit recognition, credit-scoring and prediction of the number of registered trucks. We have learned a lot of lessons from this project which have effected our application oriented research in the field of Machine Learning (ML) in Daimler-Benz. We have distinguished that, especially, more research is necessary to prepare the ML-algorithms to handle the real industrial and commercial applications. In this paper we describe, shortly, the Daimler-Benz applications in StatLog, we discuss shortcomings of the applied ML-algorithms and finally we outline the fields where we think further research is necessary. 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.

Above are papers related to the following paper: [Paper_ID] Paper #8699. 	[Title] Theory Revision in Fault Hierarchies [Abstract] The fault hierarchy representation is widely used in expert systems for the diagnosis of complex mechanical devices. On the assumption that an appropriate bias for a knowledge representation language is also an appropriate bias for learning in this domain, we have developed a theory revision method that operates directly on a fault hierarchy. This task presents several challenges: A typical training instance is missing most feature values, and the pattern of missing features is significant, rather than merely an effect of noise. Moreover, the accuracy of a candidate theory is measured by considering both the sequence of tests required to arrive at a diagnosis and its agreement with the diagnostic endpoints provided by an expert. This paper first describes the algorithm for theory revision of fault hierarchies that was designed to address these challenges, then discusses its application in knowledge base maintenance and reports on experiments that use to revise a fielded diagnostic system. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1153148. 	[Title] Learning Classification Trees [Abstract] Algorithms for learning classification trees have had successes in artificial intelligence and statistics over many years. This paper outlines how a tree learning algorithm can be derived using Bayesian statistics. This introduces Bayesian techniques for splitting, smoothing, and tree averaging. The splitting rule is similar to Quinlan's information gain, while smoothing and averaging replace pruning. Comparative experiments with reimplementations of a minimum encoding approach, Quinlan's C4 (Quinlan et al., 1987) and Breiman et al.'s CART (Breiman et al., 1984) show the full Bayesian algorithm can produce Publication: This paper is a final draft submitted for publication to the Statistics and Computing journal; a version with some minor changes appeared in Volume 2, 1992, pages 63-73. more accurate predictions than versions of these other approaches, though pay a computational price. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1135358. 	[Title] 0 Inductive learning of compact rule sets by using efficient hypotheses reduction [Abstract] A method is described which reduces the hypotheses space with an efficient and easily interpretable reduction criteria called a - reduction. A learning algorithm is described based on a - reduction and analyzed by using probability approximate correct learning results. The results are obtained by reducing a rule set to an equivalent set of kDNF formulas. The goal of the learning algorithm is to induce a compact rule set describing the basic dependencies within a set of data. The reduction is based on criterion which is very exible and gives a semantic interpretation of the rules which fulfill the criteria. Comparison with syntactical hypotheses reduction show that the a reduction improves search and has a smaller probability of missclassification. Please summarise all the given information.

Paper #1110947 has the title `Lazy Induction Triggered by CBR  
` and the 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. 
`.
Question: Which category should Paper #1110947 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1110947 has the title `Lazy Induction Triggered by CBR  
` and the 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. 
`.
Question: Which category should Paper #1110947 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: Case_Based