Above are papers related to the following paper: [Paper_ID] Paper #105899. 	[Title] In:  A Mixture Model System for Medical and Machine Diagnosis [Abstract] Diagnosis of human disease or machine fault is a missing data problem since many variables are initially unknown. Additional information needs to be obtained. The joint probability distribution of the data can be used to solve this problem. We model this with mixture models whose parameters are estimated by the EM algorithm. This gives the benefit that missing data in the database itself can also be handled correctly. The request for new information to refine the diagnosis is performed using the maximum utility principle. Since the system is based on learning it is domain independent and less labor intensive than expert systems or probabilistic networks. An example using a heart disease database is presented. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #46468. 	[Title] A Statistical Approach to Decision Tree Modeling [Abstract] A statistical approach to decision tree modeling is described. In this approach, each decision in the tree is modeled parametrically as is the process by which an output is generated from an input and a sequence of decisions. The resulting model yields a likelihood measure of goodness of fit, allowing ML and MAP estimation techniques to be utilized. An efficient algorithm is presented to estimate the parameters in the tree. The model selection problem is presented and several alternative proposals are considered. A hidden Markov version of the tree is described for data sequences that have temporal dependencies. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #24966. 	[Title] A Practical Bayesian Framework for Backprop Networks [Abstract] A quantitative and practical Bayesian framework is described for learning of mappings in feedforward networks. The framework makes possible: (1) objective comparisons between solutions using alternative network architectures; (2) objective stopping rules for network pruning or growing procedures; (3) objective choice of magnitude and type of weight decay terms or additive regularisers (for penalising large weights, etc.); (4) a measure of the effective number of well-determined parameters in a model; (5) quantified estimates of the error bars on network parameters and on network output; (6) objective comparisons with alternative learning and interpolation models such as splines and radial basis functions. The Bayesian `evidence' automatically embodies `Occam's razor,' penalising over-flexible and over-complex models. The Bayesian approach helps detect poor underlying assumptions in learning models. For learning models well matched to a problem, a good correlation between generalisation ability and the Bayesian evidence is obtained. This paper makes use of the Bayesian framework for regularisation and model comparison described in the companion paper `Bayesian interpolation' (MacKay, 1991a). This framework is due to Gull and Skilling (Gull, 1989a). Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1111304. 	[Title] Using Temporal-Difference Reinforcement Learning to Improve Decision-Theoretic Utilities for Diagnosis [Abstract] Probability theory represents and manipulates uncertainties, but cannot tell us how to behave. For that we need utility theory which assigns values to the usefulness of different states, and decision theory which concerns optimal rational decisions. There are many methods for probability modeling, but few for learning utility and decision models. We use reinforcement learning to find the optimal sequence of questions in a diagnosis situation while maintaining a high accuracy. Automated diagnosis on a heart-disease domain is used to demonstrate that temporal-difference learning can improve diagnosis. On the Cleveland heart-disease database our results are better than those reported from all previous methods. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1118092. 	[Title] Q2: Memory-based active learning for optimizing noisy continuous functions field expands beyond prediction and function [Abstract] This paper introduces a new algorithm, Q2, for optimizing the expected output of a multi-input noisy continuous function. Q2 is designed to need only a few experiments, it avoids strong assumptions on the form of the function, and it is autonomous in that it requires little problem-specific tweaking. Four existing approaches to this problem (response surface methods, numerical optimization, supervised learning, and evolutionary methods) all have inadequacies when the requirement of "black box" behavior is combined with the need for few experiments. Q2 uses instance-based determination of a convex region of interest for performing experiments. In conventional instance-based approaches to learning, a neighborhood was defined by proximity to a query point. In contrast, Q2 defines the neighborhood by a new geometric procedure that captures the size and Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1126029. 	[Title] Exploration in Active Learning [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #75674. 	[Title] Exploration Bonuses and Dual Control [Abstract] Finding the Bayesian balance between exploration and exploitation in adaptive optimal control is in general intractable. This paper shows how to compute suboptimal estimates based on a certainty equivalence approximation arising from a form of dual control. This systematizes and extends existing uses of exploration bonuses in reinforcement learning (Sutton, 1990). The approach has two components: a statistical model of uncertainty in the world and a way of turning this into exploratory behaviour. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #395547. 	[Title] EM Algorithms for PCA and SPCA [Abstract] I present an expectation-maximization (EM) algorithm for principal component analysis (PCA). The algorithm allows a few eigenvectors and eigenvalues to be extracted from large collections of high dimensional data. It is computationally very efficient in space and time. It also naturally accommodates missing information. I also introduce a new variant of PCA called sensible principal component analysis (SPCA) which defines a proper density model in the data space. Learning for SPCA is also done with an EM algorithm. I report results on synthetic and real data showing that these EM algorithms correctly and efficiently find the leading eigenvectors of the covariance of datasets in a few iterations using up to hundreds of thousands of datapoints in thousands of dimensions. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #195361. 	[Title] On Step-Size and Bias in Temporal-Difference Learning [Abstract] We present results for three new algorithms for setting the step-size parameters, ff and , of temporal-difference learning methods such as TD(). The overall task is that of learning to predict the outcome of an unknown Markov chain based on repeated observations of its state trajectories. The new algorithms select step-size parameters online in such a way as to eliminate the bias normally inherent in temporal-difference methods. We compare our algorithms with conventional Monte Carlo methods. Monte Carlo methods have a natural way of setting the step size: for each state s they use a step size of 1=n s , where n s is the number of times state s has been visited. We seek and come close to achieving comparable step-size algorithms for TD(). One new algorithm uses a = 1=n s schedule to achieve the same effect as processing a state backwards with TD(0), but remains completely incremental. Another algorithm uses a at each time equal to the estimated transition probability of the current transition. We present empirical results showing improvement in convergence rate over Monte Carlo methods and conventional TD(). A limitation of our results at present is that they apply only to tasks whose state trajectories do not contain cycles. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #116021. 	[Title] Improving Generalization with Active Learning [Abstract] Active learning differs from passive "learning from examples" in that the learning algorithm assumes at least some control over what part of the input domain it receives information about. In some situations, active learning is provably more powerful that learning from examples alone, giving better generalization for a fixed number of training examples. In this paper, we consider the problem of learning a binary concept in the absence of noise (Valiant 1984). We describe a formalism for active concept learning called selective sampling, and show how it may be approximately implemented by a neural network. In selective sampling, a learner receives distribution information from the environment and queries an oracle on parts of the domain it considers "useful." We test our implementation, called an SG-network, on three domains, and observe significant improvement in generalization. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1104182. 	[Title] Finding Promising Exploration Regions by Weighting Expected Navigation Costs continuous environments, some first-order approximations to [Abstract] In many learning tasks, data-query is neither free nor of constant cost. Often the cost of a query depends on the distance from the current location in state space to the desired query point. This is easiest to visualize in robotics environments where a robot must physically move to a location in order to learn something there. The cost of this learning is the time and effort it takes to reach the new location. Furthermore, this cost is characterized by a distance relationship: When the robot moves as directly as possible from a source state to a destination state, the states through which it passes are closer (i.e., cheaper to reach) than is the destination state. Distance relationships hold in many real-world non-robotics tasks also | any environment where states are not immediately accessible. Optimiz- ing the performance of a chemical plant, for example, requires the adjustment of analog controls which have a continuum of intermediate states. Querying possibly optimal regions of state space in these environments is inadvisable if the path to the query point intersects a region of known volatility. In discrete environments with small numbers of states, it's possible to keep track of precisely where and to what degree learning has already been done sufficiently and where it still needs to be done. It is also possible to keep best known estimates of the distances from each state to each other (see Kaelbling, 1993). Kael- bling's DG-learning algorithm is based on Floyd's all- pairs shortest-path algorithm (Aho, Hopcroft, & Ull- man 1983) and is just slightly different from that used here. These "all-goals" algorithms (after Kaelbling) can provide a highly satisfying representation of the distance/benefit tradeoff. where E x is the exploration value of state x (the potential benefit of exploring state x), D xy is the distance to state y, and A xy is the action to take in state x to move most cheaply to state y. This information can be learned incrementally and completely : That is, it can be guaranteed that if a path from any state x to any state y is deducible from the state transitions seen so far, then (1) the algorithm will have a non-null entry for S xy (i.e., the algorithm will know a path from x to y), and (2) The current value for D xy will be the best deducible value from all data seen so far. With this information, decisions about which areas to explore next can be based on not just the amount to be gained from such exploration but also on the cost of reaching each area together with the benefit of incidental exploration done on the way. Though optimal exploration is NP-hard (i.e., it's at least as difficult as TSP) good approximations are easily computable. One such good approximation is to take the action at each state that leads in the direction of greatest accumulated exploration benefit: Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #18619. 	[Title] Heterogeneous Uncertainty Sampling for Supervised Learning [Abstract] Uncertainty sampling methods iteratively request class labels for training instances whose classes are uncertain despite the previous labeled instances. These methods can greatly reduce the number of instances that an expert need label. One problem with this approach is that the classifier best suited for an application may be too expensive to train or use during the selection of instances. We test the use of one classifier (a highly efficient probabilistic one) to select examples for training another (the C4.5 rule induction program). Despite being chosen by this heterogeneous approach, the uncertainty samples yielded classifiers with lower error rates than random samples ten times larger. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #686015. 	[Title] "What is the best thing to do right now?": getting beyond greedy exploration [Abstract]  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 #23116. 	[Title] Bayesian Methods for Adaptive Models [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #686030. 	[Title] Advances in Neural Information Processing Systems 8 Active Learning in Multilayer Perceptrons [Abstract] We propose an active learning method with hidden-unit reduction, which is devised specially for multilayer perceptrons (MLP). First, we review our active learning method, and point out that many Fisher-information-based methods applied to MLP have a critical problem: the information matrix may be singular. To solve this problem, we derive the singularity condition of an information matrix, and propose an active learning technique that is applicable to Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #28254. 	[Title] In  Active Learning with Statistical Models [Abstract] For many types of learners one can compute the statistically "optimal" way to select data. We review how these techniques have been used with feedforward neural networks [MacKay, 1992; Cohn, 1994]. We then show how the same principles may be used to select data for two alternative, statistically-based learning architectures: mixtures of Gaussians and locally weighted regression. While the techniques for neural networks are expensive and approximate, the techniques for mixtures of Gaussians and locally weighted regres sion are both efficient and accurate. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #27623. 	[Title] Neural Network Exploration Using Optimal Experiment Design [Abstract] We consider the question "How should one act when the only goal is to learn as much as possible?" Building on the theoretical results of Fedorov [1972] and MacKay [1992], we apply techniques from Optimal Experiment Design (OED) to guide the query/action selection of a neural network learner. We demonstrate that these techniques allow the learner to minimize its generalization error by exploring its domain efficiently and completely. We conclude that, while not a panacea, OED-based query/action has much to offer, especially in domains where its high computational costs can be tolerated. This report describes research done at the Center for Biological and Computational Learning and the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for the Center is provided in part by a grant from the National Science Foundation under contract ASC-9217041. The author was also funded by ATR Human Information Processing Laboratories, Siemens Corporate Research and NSF grant CDA-9309300. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #4329. 	[Title] Supervised learning from incomplete data via an EM approach [Abstract] Real-world learning tasks may involve high-dimensional data sets with arbitrary patterns of missing data. In this paper we present a framework based on maximum likelihood density estimation for learning from such data sets. We use mixture models for the density estimates and make two distinct appeals to the Expectation-Maximization (EM) principle (Dempster et al., 1977) in deriving a learning algorithm|EM is used both for the estimation of mixture components and for coping with missing data. The resulting algorithm is applicable to a wide range of supervised as well as unsupervised learning problems. Results from a classification benchmark|the iris data set|are presented. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #4330. 	[Title] Hierarchical Mixtures of Experts and the EM Algorithm [Abstract] We present a tree-structured architecture for supervised learning. The statistical model underlying the architecture is a hierarchical mixture model in which both the mixture coefficients and the mixture components are generalized linear models (GLIM's). Learning is treated as a maximum likelihood problem; in particular, we present an Expectation-Maximization (EM) algorithm for adjusting the parameters of the architecture. We also develop an on-line learning algorithm in which the parameters are updated incrementally. Comparative simulation results are presented in the robot dynamics domain. This report describes research done at the Dept. of Brain and Cognitive Sciences, the Center for Biological and Computational Learning, and the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for CBCL is provided in part by a grant from the NSF (ASC-9217041). Support for the laboratory's artificial intelligence research is provided in part by the Advanced Research Projects Agency of the Dept. of Defense. The authors were supported by a grant from the McDonnell-Pew Foundation, by a grant from ATR Human Information Processing Research Laboratories, by a grant from Siemens Corporation, by by grant IRI-9013991 from the National Science Foundation, by grant N00014-90-J-1942 from the Office of Naval Research, and by NSF grant ECS-9216531 to support an Initiative in Intelligent Control at MIT. Michael I. Jordan is a NSF Presidential Young Investigator. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #27627. 	[Title] Information-based objective functions for active data selection [Abstract] Learning can be made more efficient if we can actively select particularly salient data points. Within a Bayesian learning framework, objective functions are discussed which measure the expected informativeness of candidate measurements. Three alternative specifications of what we want to gain information about lead to three different criteria for data selection. All these criteria depend on the assumption that the hypothesis space is correct, which may prove to be their main weakness. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #686061. 	[Title] An Algorithm for Active Data Collection for Learning Feasibility Study with Neural Networks. [Abstract] Macquarie University Technical Report No. 95-173C Department of Computing School of MPCE, Macquarie University, New South Wales, Australia Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #27631. 	[Title] Control Systems Magazine, 14, 1, pp.57-71. Robot Juggling: An Implementation of Memory-based Learning [Abstract] This paper explores issues involved in implementing robot learning for a challenging dynamic task, using a case study from robot juggling. We use a memory-based local model - ing approach (locally weighted regression) to represent a learned model of the task to be performed. Statistical tests are given to examine the uncertainty of a model, to optimize its pre diction quality, and to deal with noisy and corrupted data. We develop an exploration algorithm that explicitly deals with prediction accuracy requirements dur ing explo - ration. Using all these ingredients in combination with methods from optimal control, our robot achieves fast real - time learning of the task within 40 to 100 trials. * Address of both authors: Massachusetts Institute of Technology, The Artificial Intelligence Laboratory & The Department of Brain and Cognitive Sciences, 545 Technology Square, Cambride, MA 02139, USA. Email: ss-chaal@ai.mit.edu, cga@ai.mit.edu. Support was provided by the Air Force Office of Sci entific Research and by Siemens Cor pora tion. Support for the first author was provided by the Ger man Scholar ship Foundation and the Alexander von Hum boldt Founda tion. Support for the second author was provided by a Na tional Sci ence Foundation Pre sidential Young Investigator Award. We thank Gideon Stein for im ple ment ing the first version of LWR on the i860 microprocessor, and Gerrie van Zyl for build ing the devil stick robot and implementing the first version of devil stick learning. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #27632. 	[Title] REINFORCEMENT DRIVEN INFORMATION ACQUISITION IN NON-DETERMINISTIC ENVIRONMENTS [Abstract] For an agent living in a non-deterministic Markov environment (NME), what is, in theory, the fastest way of acquiring information about its statistical properties? The answer is: to design "optimal" sequences of "experiments" by performing action sequences that maximize expected information gain. This notion is implemented by combining concepts from information theory and reinforcement learning. Experiments show that the resulting method, reinforcement driven information acquisition, can explore certain NMEs much faster than conventional random exploration. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1110768. 	[Title] Recurrent Neural Networks for Missing or Asynchronous Data [Abstract] In this paper we propose recurrent neural networks with feedback into the input units for handling two types of data analysis problems. On the one hand, this scheme can be used for static data when some of the input variables are missing. On the other hand, it can also be used for sequential data, when some of the input variables are missing or are available at different frequencies. Unlike in the case of probabilistic models (e.g. Gaussian) of the missing variables, the network does not attempt to model the distribution of the missing variables given the observed variables. Instead it is a more "discriminant" approach that fills in the missing variables for the sole purpose of minimizing a learning criterion (e.g., to minimize an output error). 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 #510718. 	[Title] Learning Bayesian Networks from Incomplete Data [Abstract] Much of the current research in learning Bayesian Networks fails to effectively deal with missing data. Most of the methods assume that the data is complete, or make the data complete using fairly ad-hoc methods; other methods do deal with missing data but learn only the conditional probabilities, assuming that the structure is known. We present a principled approach to learn both the Bayesian network structure as well as the conditional probabilities from incomplete data. The proposed algorithm is an iterative method that uses a combination of Expectation-Maximization (EM) and Imputation techniques. Results are presented on synthetic data sets which show that the performance of the new algorithm is much better than ad-hoc methods for handling missing data. Please summarise all the given information.

Paper #105899 has the title `In:  A Mixture Model System for Medical and Machine Diagnosis  
` and the abstract `Diagnosis of human disease or machine fault is a missing data problem since many variables are initially unknown. Additional information needs to be obtained. The joint probability distribution of the data can be used to solve this problem. We model this with mixture models whose parameters are estimated by the EM algorithm. This gives the benefit that missing data in the database itself can also be handled correctly. The request for new information to refine the diagnosis is performed using the maximum utility principle. Since the system is based on learning it is domain independent and less labor intensive than expert systems or probabilistic networks. An example using a heart disease database is presented.
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Question: Which category should Paper #105899 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #105899 has the title `In:  A Mixture Model System for Medical and Machine Diagnosis  
` and the abstract `Diagnosis of human disease or machine fault is a missing data problem since many variables are initially unknown. Additional information needs to be obtained. The joint probability distribution of the data can be used to solve this problem. We model this with mixture models whose parameters are estimated by the EM algorithm. This gives the benefit that missing data in the database itself can also be handled correctly. The request for new information to refine the diagnosis is performed using the maximum utility principle. Since the system is based on learning it is domain independent and less labor intensive than expert systems or probabilistic networks. An example using a heart disease database is presented.
`.
Question: Which category should Paper #105899 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: Probabilistic_Methods