Above are papers related to the following paper: [Paper_ID] Paper #45212. 	[Title] Gain Adaptation Beats Least Squares? [Abstract] I present computational results suggesting that gain-adaptation algorithms based in part on connectionist learning methods may improve over least squares and other classical parameter-estimation methods for stochastic time-varying linear systems. The new algorithms are evaluated with respect to classical methods along three dimensions: asymptotic error, computational complexity, and required prior knowledge about the system. The new algorithms are all of the same order of complexity as LMS methods, O(n), where n is the dimensionality of the system, whereas least-squares methods and the Kalman filter are O(n 2 ). The new methods also improve over the Kalman filter in that they do not require a complete statistical model of how the system varies over time. In a simple computational experiment, the new methods are shown to produce asymptotic error levels near that of the optimal Kalman filter and significantly below those of least-squares and LMS methods. The new methods may perform better even than the Kalman filter if there is any error in the filter's model of how the system varies over time. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #235776. 	[Title] Recursive Automatic Algorithm Selection for Inductive Learning [Abstract] COINS Technical Report 94-61 August 1994 Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1105764. 	[Title] Least-Squares Temporal Difference Learning [Abstract] Submitted to NIPS-98 TD() is a popular family of algorithms for approximate policy evaluation in large MDPs. TD() works by incrementally updating the value function after each observed transition. It has two major drawbacks: it makes inefficient use of data, and it requires the user to manually tune a stepsize schedule for good performance. For the case of linear value function approximations and = 0, the Least-Squares TD (LSTD) algorithm of Bradtke and Barto [5] eliminates all stepsize parameters and improves data efficiency. This paper extends Bradtke and Barto's work in three significant ways. First, it presents a simpler derivation of the LSTD algorithm. Second, it generalizes from = 0 to arbitrary values of ; at the extreme of = 1, the resulting algorithm is shown to be a practical formulation of supervised linear regression. Third, it presents a novel, intuitive interpretation of LSTD as a model-based reinforcement learning technique. 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 #6214. 	[Title] Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming [Abstract] This paper extends previous work with Dyna, a class of architectures for intelligent systems based on approximating dynamic programming methods. Dyna architectures integrate trial-and-error (reinforcement) learning and execution-time planning into a single process operating alternately on the world and on a learned model of the world. In this paper, I present and show results for two Dyna architectures. The Dyna-PI architecture is based on dynamic programming's policy iteration method and can be related to existing AI ideas such as evaluation functions and universal plans (reactive systems). Using a navigation task, results are shown for a simple Dyna-PI system that simultaneously learns by trial and error, learns a world model, and plans optimal routes using the evolving world model. The Dyna-Q architecture is based on Watkins's Q-learning, a new kind of reinforcement learning. Dyna-Q uses a less familiar set of data structures than does Dyna-PI, but is arguably simpler to implement and use. We show that Dyna-Q architectures are easy to adapt for use in changing environments. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #46887. 	[Title] Learning Polynomial Functions by Feature Construction [Abstract] We present a method for learning higher-order polynomial functions from examples using linear regression and feature construction. Regression is used on a set of training instances to produce a weight vector for a linear function over the feature set. If this hypothesis is imperfect, a new feature is constructed by forming the product of the two features that most effectively predict the squared error of the current hypothesis. The algorithm is then repeated. In an extension to this method, the specific pair of features to combine is selected by measuring their joint ability to predict the hypothesis' error. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6311. 	[Title] Dynamic Automatic Model Selection [Abstract] COINS Technical Report 92-30 February 1992 Abstract The problem of how to learn from examples has been studied throughout the history of machine learning, and many successful learning algorithms have been developed. A problem that has received less attention is how to select which algorithm to use for a given learning task. The ability of a chosen algorithm to induce a good generalization depends on how appropriate the model class underlying the algorithm is for the given task. We define an algorithm's model class to be the representation language it uses to express a generalization of the examples. Supervised learning algorithms differ in their underlying model class and in how they search for a good generalization. Given this characterization, it is not surprising that some algorithms find better generalizations for some, but not all tasks. Therefore, in order to find the best generalization for each task, an automated learning system must search for the appropriate model class in addition to searching for the best generalization within the chosen class. This thesis proposal investigates the issues involved in automating the selection of the appropriate model class. The presented approach has two facets. Firstly, the approach combines different model classes in the form of a model combination decision tree, which allows the best representation to be found for each subconcept of the learning task. Secondly, which model class is the most appropriate is determined dynamically using a set of heuristic rules. Explicit in each rule are the conditions in which a particular model class is appropriate and if it is not, what should be done next. In addition to describing the approach, this proposal describes how the approach will be evaluated in order to demonstrate that it is both an efficient and effective method for automatic model selection. 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 #28202. 	[Title] MultiPlayer Residual Advantage Learning With General Function Approximation [Abstract] A new algorithm, advantage learning, is presented that improves on advantage updating by requiring that a single function be learned rather than two. Furthermore, advantage learning requires only a single type of update, the learning update, while advantage updating requires two different types of updates, a learning update and a normilization update. The reinforcement learning system uses the residual form of advantage learning. An application of reinforcement learning to a Markov game is presented. The testbed has continuous states and nonlinear dynamics. The game consists of two players, a missile and a plane; the missile pursues the plane and the plane evades the missile. On each time step , each player chooses one of two possible actions; turn left or turn right, resulting in a 90 degree instantaneous change in the aircraft s heading. Reinforcement is given only when the missile hits the plane or the plane reaches an escape distance from the missile. The advantage function is stored in a single-hidden-layer sigmoidal network. Speed of learning is increased by a new algorithm , Incremental Delta-Delta (IDD), which extends Jacobs (1988) Delta-Delta for use in incremental training, and differs from Suttons Incremental Delta-Bar-Delta (1992) in that it does not require the use of a trace and is amenable for use with general function approximation systems. The advantage learning algorithm for optimal control is modified for games in order to find the minimax point, rather than the maximum. Empirical results gathered using the missile/aircraft testbed validate theory that suggests residual forms of reinforcement learning algorithms converge to a local minimum of the mean squared Bellman residual when using general function approximation systems. Also, to our knowledge, this is the first time an approximate second order method has been used with residual algorithms. Empirical results are presented comparing convergence rates with and without the use of IDD for the reinforcement learning testbed described above and for a supervised learning testbed. The results of these experiments demonstrate IDD increased the rate of convergence and resulted in an order of magnitude lower total asymptotic error than when using backpropagation alone. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1111788. 	[Title] Multivariate Decision Trees [Abstract] COINS Technical Report 92-82 December 1992 Abstract Multivariate decision trees overcome a representational limitation of univariate decision trees: univariate decision trees are restricted to splits of the instance space that are orthogonal to the feature's axis. This paper discusses the following issues for constructing multivariate decision trees: representing a multivariate test, including symbolic and numeric features, learning the coefficients of a multivariate test, selecting the features to include in a test, and pruning of multivariate decision trees. We present some new and review some well-known methods for forming multivariate decision trees. The methods are compared across a variety of learning tasks to assess each method's ability to find concise, accurate decision trees. The results demonstrate that some multivariate methods are more effective than others. In addition, the experiments confirm that allowing multivariate tests improves the accuracy of the resulting decision tree over univariate trees. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #120013. 	[Title] From Isolation to Cooperation: An Alternative View of a System of Experts [Abstract] We introduce a constructive, incremental learning system for regression problems that models data by means of locally linear experts. In contrast to other approaches, the experts are trained independently and do not compete for data during learning. Only when a prediction for a query is required do the experts cooperate by blen ding their individual predictions. Each expert is trained by minimizing a penalized local cross validation error using second order methods. In this way, an expert is able to adjust the size and shape of the receptive field in which its predictions are valid, and also to adjust its bias on the importance of individual input dimensions. The size and shape adjustment corresponds to finding a local distance metric, while the bias adjustment accomplishes local dimensio n-ality reduction. We derive asymptotic results for our method. In a variety of simulations we demonstrate the properties of the algorithm with respect to interference, learning speed, prediction accuracy, feature detection, and task or i-ented incremental learning. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1153106. 	[Title] Classification of EEG Signals Using a Sparse Polynomial Builder [Abstract] Edward S. Orosz and Charles W. Anderson Technical Report CS-94-111 April 27, 1994 Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #72406. 	[Title] A Comparison of Direct and Model-Based Reinforcement Learning [Abstract] This paper compares direct reinforcement learning (no explicit model) and model-based reinforcement learning on a simple task: pendulum swing up. We find that in this task model-based approaches support reinforcement learning from smaller amounts of training data and efficient handling of changing goals. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #7419. 	[Title] A Neuro-Dynamic Programming Approach to Retailer Inventory Management 1 [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #25791. 	[Title] Adapting Bias by Gradient Descent: An Incremental Version of Delta-Bar-Delta [Abstract] Appropriate bias is widely viewed as the key to efficient learning and generalization. I present a new algorithm, the Incremental Delta-Bar-Delta (IDBD) algorithm, for the learning of appropriate biases based on previous learning experience. The IDBD algorithm is developed for the case of a simple, linear learning system|the LMS or delta rule with a separate learning-rate parameter for each input. The IDBD algorithm adjusts the learning-rate parameters, which are an important form of bias for this system. Because bias in this approach is adapted based on previous learning experience, the appropriate testbeds are drifting or non-stationary learning tasks. For particular tasks of this type, I show that the IDBD algorithm performs better than ordinary LMS and in fact finds the optimal learning rates. The IDBD algorithm extends and improves over prior work by Jacobs and by me in that it is fully incremental and has only a single free parameter. This paper also extends previous work by presenting a derivation of the IDBD algorithm as gradient descent in the space of learning-rate parameters. Finally, I offer a novel interpretation of the IDBD algorithm as an incremental form of hold-one-out cross validation. Please summarise all the given information.

Paper #45212 has the title `Gain Adaptation Beats Least Squares?  
` and the abstract `I present computational results suggesting that gain-adaptation algorithms based in part on connectionist learning methods may improve over least squares and other classical parameter-estimation methods for stochastic time-varying linear systems. The new algorithms are evaluated with respect to classical methods along three dimensions: asymptotic error, computational complexity, and required prior knowledge about the system. The new algorithms are all of the same order of complexity as LMS methods, O(n), where n is the dimensionality of the system, whereas least-squares methods and the Kalman filter are O(n 2 ). The new methods also improve over the Kalman filter in that they do not require a complete statistical model of how the system varies over time. In a simple computational experiment, the new methods are shown to produce asymptotic error levels near that of the optimal Kalman filter and significantly below those of least-squares and LMS methods. The new methods may perform better even than the Kalman filter if there is any error in the filter's model of how the system varies over time. 
`.
Question: Which category should Paper #45212 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #45212 has the title `Gain Adaptation Beats Least Squares?  
` and the abstract `I present computational results suggesting that gain-adaptation algorithms based in part on connectionist learning methods may improve over least squares and other classical parameter-estimation methods for stochastic time-varying linear systems. The new algorithms are evaluated with respect to classical methods along three dimensions: asymptotic error, computational complexity, and required prior knowledge about the system. The new algorithms are all of the same order of complexity as LMS methods, O(n), where n is the dimensionality of the system, whereas least-squares methods and the Kalman filter are O(n 2 ). The new methods also improve over the Kalman filter in that they do not require a complete statistical model of how the system varies over time. In a simple computational experiment, the new methods are shown to produce asymptotic error levels near that of the optimal Kalman filter and significantly below those of least-squares and LMS methods. The new methods may perform better even than the Kalman filter if there is any error in the filter's model of how the system varies over time. 
`.
Question: Which category should Paper #45212 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: Neural_Networks