 | Abductive Explanation-Based Learning: A Solution to the Multiple Inconsistent Explanation Problem - William W. Cohen - 1992 |
| Abnormal data points in the data set: an algorithm for robust neural net regression - Yong Liu - 1997 |
| Absolute error bounds for learning linear functions online - E. J. Bernstein - 1992 |
| Abstract Combinatorial Characterizations of Exact Learning via Queries - Jose Luis Balcázar, Jorge Castro and David Guijarro - 2000 |
| Abstract Inference - U. Grenander - 1981 |
| Abstracting from robot sensor data using hidden Markov models - Laura Firoiu and Paul R. Cohen - 1999 |
| Accelerated Backpropagation Learning: Two Optimization Methods - R. Battiti - August 1989 |
| An accelerated Chow and Liu algorithm: fitting tree distributions to high-dimensional sparse data - Marina Meila - 1999 |
| Accelerated Learning in Layered Neural Networks - S. A. Solla, E. Levin and M. Fleisher - 1988 |
| Accelerating EM for Large Databases - Bo Thiesson, Christopher Meek and David Heckerman - 2001 |
| Acceleration of learning in binary choice problems - Y. Kabashima and S. Shinomoto - 1993 |
| ACE: a syntax-directed editor customizable from examples and queries - Y. Takada, Y. Sakakibara and T. Ohtani - 1992 |
| Achieving Efficient and Congnitively Plausible Learning in Backgammon - Scott Sanner, John R. Anderson, Christian Lebiere and Marsha Lovett - 2000 |
| Acquiring and Combining Overlapping Concepts - Joel D. Martin and Dorrit O. Billman - 1994 |
| Acquiring Recursive and Iterative Concepts with Explanation-Based Learning - Jude W. Shavlik - 1990 |
| Acquisition of Children's Addition Strategies: A Model of Impasse-Free, Knowledge-Level Learning - Randolph M. Jones and Kurt Vanlehn - 1994 |
| Acquisition of Stand-up Behavior by a Real Robot using Hierarchical Reinforcement Learning - Jun Morimoto and Kenji Doya - 2000 |
| The Acquisition of Syntatic Knowledge - Robert Berwick - 1985 |
| Active exploration and learning in real-valued spaces using multi-armed bandit allocation indices - Marcos Salganicoff and Lyle H. Ungar - 1995 |
| Active learning for natural language parsing and information extraction - Cynthia A. Thompson, Mary Elaine Califf and Raymond J. Mooney - 1999 |
| Active Learning for Vision-Based Robot Grasping - Marcos Salganicoff, Lyle H. Ungar and Ruzena Bajcsy - 1996 |
| Active Learning Using Arbitrary Binary Valued Queries - S. R. Kulkarni, S. K. Mitter and J. N. Tsitsiklis - 1993 |
| ACT-R and learning - John R. Anderson - 1999 |
| Actual return reinforcement learning versus temporal differences: some theoretical and experimental results - Mark D. Pendrith and Malcolm R. K. Ryan - 1996 |
| AdaCost: misclassification cost-sensitive boosting - Wei Fan, Salvatore J. Stolfo, Junxin Zhang and Philip K. Chan - 1999 |
| Adaptation in Natural and Artificial Systems - J. H. Holland - 1975 |
| An adaptation of Relief for attribute estimation in regression - Marko Robnik-ťikonja and Igor Kononenko - 1997 |
| Adaptive and Self-Confident On-Line Learning Algorithms - Peter Auer and Claudio Gentile - 2000 |
| Adaptive Filter Theory - S. Haykin - 1991 |
| An adaptive network that constructs and uses an internal model of its world - R. S. Sutton and A. G. Barto - 1985 |
| Adaptive Neural Networks Using MOS Charge Storage - D. B. Schwartz, R. E. Howard and W. E. Hubbard - 1989 |
| An Adaptive Optimal Controller for Discrete-Time Markov Environments - I. H. Witten - 1977 |
| Adaptive probabilistic networks with hidden variables - John Binder, Daphne Koller, Stuart Russell and Keiji Kanazawa - 1997 |
| Adaptive Query Shifting for Content-Based Image Retrieval - Giorgio Giacinto, Fabio Roli and Giorgio Fumera - 2001 |
| An Adaptive Regularization Criterion for Supervised Learning - Dale Schuurmans and Finnegan Southey - 2000 |
| Adaptive Resolution Model-Free Reinforcement Learning: Decision Boundary Partitioning - Stuart I. Reynolds - 2000 |
| Adaptive Retrieval Agents: Internalizing Local Context and Scaling up to the Web - Filippo Menczer and Richard K. Belew - 2000 |
| Adaptive Signal Processing - B. Widrow and S. Stearns - 1985 |
| Adaptive Stochastic Cellular Automata: Theory - Y. C. Lee, S. Qian, R. D. Jones, C. W. Barnes, G. W. Flake, M. K. O'Rourke, K. Lee, H. H. Chen, G. Z. Sun, Y. Q. Zhang, D. Chen and C. L. Giles - 1990 |
| Adaptive Stochastic Cellular Automata: Theory - S. Qian, Y. C. Lee, R. D. Jones, C. W. Barnes, G. W. Flake, M. K. O'Rourke, K. Lee, H. H. Chen, G. Z. Sun, Y. Q. Zhang, D. Chen and C. L. Giles - 1990 |
| Adaptive Strategies and Regret Minimization in Arbitrarily Varying Markov Environments - Shie Mannor and Nahum Shimkin - 2001 |
| Adaptive Switching Circuits - B. Widrow and M. E. Hoff - 1960 |
| An Adaptive Version of the Boost by Majority Algorithm - Yoav Freund - 2001 |
| An adaptive version of the boost by majority algorithm - Yoav Freund - 1999 |
| Adaptive Versus Nonadaptive Attribute-Efficient Learning - Peter Damaschke - 2000 |
| Additive models, boosting, and inference for generalized divergences - John Lafferty - 1999 |
| Additive versus exponentiated gradient updates for linear prediction - Jyrki Kivinen and Manfred K. Warmuth - 1995 |
| Addressing the curse of imbalanced training sets: one-sided selection - Miroslav Kubat and Stan Matwin - 1997 |
| Adjusting the Outputs of a Classifier to New a Priori Probabilities May Significantly Improve Classifiction Accuracy: Evidence from a Multi-Class Problem in Remote Sensing - Patrice Latinne, Marco Saerens and Christine Decaestecker - 2001 |
| An advanced evolution should not repeat its past errors - Caroline Ravis'e and Michèle Sebag - 1996 |
| Advice to Machine Learning Authors - Pat Langley - 1990 |
| Aggregating Inductive Expertise - Daniel N. Osherson, Michael Stob and Scott Weinstein - 1986 |
| Aggregating Inductive Expertise on Partial Recursive Functions - Anna-Maria Emde and Britta Schinzel - 1992 |
| Aggregating Strategies - V. Vovk - 1990 |
| Agnostic Boosting - Shai Ben-David, Philip M. Long and Yishay Mansour - 2001 |
| Agnostic Learning Nonconvex Function Classes - Shahar Mendelson and Robert C. Williamson - 2002 |
| Agnostic Learning of Geometric Patterns - Sally A. Goldman, Stephen S. Kwek and Stephen D. Scott - 2001 |
| Agnostic learning of geometric patterns - Sally A. Goldman, Stephen S. Kwek and Stephen D. Scott - 1997 |
| ALECSYS and the AutonoMouse: learning to control a real robot by distributed classifier systems - Marco Dorigo - 1995 |
| Algebraic Analysis for Singular Statistical Estimation - Sumio Watanabe - 1999 |
| An Algebraic Framework for Inductive Program Synthesis - K. P. Jantke - 1986 |
| Algebraic Reasoning about Reactions: Discovery of Conserved Properties in Particle Physics - Raúl E. Valdés-Pérez - 1994 |
| Algebraic Structure of some Learning Systems - Jean-Gabriel Ganascia - 1993 |
| An Algorithm for Distributed Reinforcement Learning in Cooperative Multi-Agent Systems - Martin Lauer and Martin Riedmiller - 2000 |
| An algorithm for the inference of tree grammars - R. C. Gonzalez, J. J. Edwards and M. G. Thomason - 1976 |
| Algorithmic Aspects of Boosting - Osamu Watanabe - 2001 |
| Algorithmic Learning of Formal Languages and Decision Trees - Y. Sakakibara - October 1991 |
| Algorithmic Learning Theory, 10th International Conference, ALT '99, Tokyo, Japan, December 1999, Proceedings - Osamu Watanabe and Takashi Yokomori - December 1999 |
| Algorithmic Learning Theory, 11th International Conference, ALT 2000, Sydney, Australia, December 2000, Proceedings - Hiroki Arimura and Sanjay Jain and Arun Sharma - December 2000 |
| Algorithmic Learning Theory, 12th International Conference, ALT 2001, Washington, DC, USA, November 25-28, 2001, Proceedings - Naoki Abe and Roni Khardon and Thomas Zeugmann - November 2001 |
| Algorithmic Learning Theory, 13th International Conference, ALT 2002, Lübeck, Germany, November 2002, Proceedings - Nicolò Cesa-Bianchi and Masayuki Numao and Rüdiger Reischuk - November 2002 |
| Algorithmic Learning Theory, 4th International Workshop, ALT '93, Tokyo, Japan, November 1993, Proceedings - K. P. Jantke and S. Kobayashi and E. Tomita and T. Yokomori - November 1993 |
| Algorithmic Learning Theory, 4th International Workshop on Analogical and Inductive Inference, AII '94, 5th International Workshop on Algorithmic Learning Theory, ALT '94, Reinhardsbrunn Castle, Germany, October 1994, Proceedings - Setsuo Arikawa and Klaus P. Jantke - October 1994 |
| Algorithmic Learning Theory, 6th International Workshop, ALT '95, Fukuoka, Japan, October 18-20, 1995, Proceedings - Klaus P. Jantke and Takeshi Shinohara and Thomas Zeugmann - October 1995 |
| Algorithmic Learning Theory, 7th International Workshop, ALT '96, Sydney, Australia, October 1996, Proceedings - Setsuo Arikawa and Arun K. Sharma - October 1996 |
| Algorithmic Learning Theory, 8th International Workshop, ALT '97, Sendai, Japan, October 1997, Proceedings - Ming Li and Akira Maruoka - October 1997 |
| Algorithmic Learning Theory, 9th International Conference, ALT '98, Otzenhausen, Germany, October 1998, Proceedings - Michael M. Richter and Carl H. Smith and Rolf Wiehagen and Thomas Zeugmann - October 1998 |
| Algorithmic Learning Theory, Third Workshop, ALT '92, Tokyo, Japan, October 1992, Proceedings - S. Doshita and K. Furukawa and K. P. Jantke and T. Nishida - October 1993 |
| Algorithmic Learning Theory with Elementary Formal Systems - Setsuo Arikawa, Satoru Miyano, Ayumi Shinohara, Takeshi Shinohara and Akihiro Yamamoto - 1992 |
| Algorithmic Program Debugging - E. Y. Shapiro - 1983 |
| Algorithmic stability and sanity-check bounds for leave-one-out cross-validation - Michael Kearns and Dana Ron - 1997 |
| Algorithmische Erkennung - Rolf Lindner - 1972 |
| Algorithmisches Lernen von Funktionen und Sprachen - Thomas Zeugmann - 1993 |
| Algorithms and applications for multitask learning - Rich Caruana - 1996 |
| Algorithms and Lower Bounds for On-Line Learning of Geometrical Concepts - Wolfgang Maass and György Turán - 1994 |
| Algorithm Selection using Reinforcement Learning - Michail G. Lagoudakis and Michael L. Littman - 2000 |
| Algorithms for Exploring an Unknown Graph - M. Betke - February 1992 |
| Algorithms for Inverse Reinforcement Learning - Andrew Y. Ng and Stuart Russell - 2000 |
| Algorithms for producing grammars from sample derivations: a common problem of formal language theory and developmental biology - H. Feliciangeli and G. Herman - 1977 |
| Algorithms in Combinatorial Geometry - H. Edelsbrunner - 1987 |
| An Algorithm that Learns What's in a Name - Daniel M. Bikel, Richard Schwartz and Ralph M. Weischedel - 1999 |
| An algorithm to learn read-once threshold formulas, and transformations between learning models - N. Bshouty, T. Hancock, L. Hellerstein and M. Karpinski - 1994 |
| Almost all monotone Boolean functions are polynomially learnable using membership queries - lya Shmulevich, Aleksey D. Korshunov and Jaakko Astola - 2001 |
| The alternating decision tree learning algorithm, - Yoav Freund and Llew Mason - 1999 |
| Amplification of weak learning under the uniform distribution - D. Boneh and R. Lipton - 1993 |
| Analogical and Inductive Inference, International Workshop AII '86. Wendisch-Rietz, GDR, October 1986, Proceedings - K. P. Jantke - 1987 |
| Analogical and Inductive Inference, International Workshop AII '89. Reinhardsbrunn Castle, GDR, October 1989, Proceedings - K. P. Jantke - October 1989 |
| Analogical and Inductive Inference, International Workshop AII '92. Dagstuhl Castle, Germany, October 1992, Proceedings - K. P. Jantke - October 1992 |
| Analogical logic program synthesis algorithm that can refute inappropriate similarities - Ken Sadohara and Makoto Haraguchi - 1995 |
| Analogical reasoning as a form of hypothetical reasoning and justification-based knowledge acquisition - R. Orihara - 1992 |
| Analogical reasoning using elementary formal system with mismatch - T. Miyahara - 1992 |
| Analogy access by mapping spreading and abstraction in large, multifunctional knowledge bases - Davide Roverso - 1996 |
| Analogy is NP-hard - S. Furuya and S. Miyano - 1992 |
| An analysis of actor/critic algorithms using eligibility traces: reinforcement learning with imperfect value functions - Hajime Kimura and Shigenobu Kobayashi - 1998 |
| An Analysis of a Learning Paradigm - D. Osherson, M. Stob and S. Weinstein - 1986 |
| Analysis of an Identification Algorithm Arising in the Adaptive Estimation of Markov Chains - A. Arapostathis and S. I. Marcus - 1990 |
| Analysis of a simple learning algorithm: learning foraging thresholds for lizards - Leslie Ann Goldberg, William E. Hart and David Bruce Wilson - 1996 |
| Analysis of Case-Based Representability of Boolean Functions by Monotone Theory - Ken Satoh - 1998 |
| An analysis of direct reinforcement learning in non-Markovian domains - Mark D. Pendrith and Michael J. McGarity - 1998 |
| Analysis of greedy expert hiring and an application to memory-based learning - Igal Galperin - 1996 |
| Analysis of Hidden Units in a Layered Network Trained to Classify Sonar Targets - R. P. Gorman and T. J. Sejnowski - 1988 |
| Analysis of the blurring process - Yizong Cheng and Zhangyong Wan - 1995 |
| Analysis of the Performance of AdaBoost.M2 for the Simulated Digit-Recognition-Example - Günther Eibl and Karl Peter Pfeiffer - 2001 |
| An Analysis of the WITT Algorithm - Jan L. Talmon, Herco Fonteijn and Peter J. Braspenning - 1993 |
| Analysis of two gradient-based algorithms for on-line regression - Nicolò Cesa-Bianchi - 1997 |
| An Analytic Center Machine - Theodore B. Trafalis and Alexander M. Malyscheff - 2002 |
| Analyzing Relational Learning in the Phase Transition Framework - Attilio Giordana, Lorenza Saitta, Michele Sebag and Marco Botta - 2000 |
| Analyzing the Average-Case Behavior of Conjunctive Learning Algorithms - R. Reischuk and T. Zeugmann - August 1998 |
| Angluin's theorem for indexed families of r.e. sets and applications - Dick de Jongh and Makoto Kanazawa - 1996 |
| Anomalous Learning Helps Succinctness - J. Case, S. Jain and A. Sharma - October 1990 |
| Anomalous Learning Helps Succinctness - John Case, Sanjay Jain and Arun Sharma - 1996 |
| Anomaly Detection over Noisy Data using Learned Probability Distributions - Eleazar Eskin - 2000 |
| Ant-Q:a reinforcement learning approach to the traveling salesman problem - Luca M. Gambardella and Marco Dorigo - 1995 |
| Apple Tasting - David P. Helmbold, Nicholas Littlestone and Philip M. Long - September 2000 |
| Apple tasting and nearly one-sided learning - D. P. Helmbold, N. Littlestone and P. M. Long - 1992 |
| Application of a General Learning Algorithm to the Control of Robotic Manipulators - W. T. Miller, F. H. Glanz and L. G. Kraft - 1987 |
| An application of Bernstein polynomials in PAC model - Masahiro Matsuoka - 1993 |
| An Application of Codes to Attribute-Efficient Learning - Thomas Hofmeister - 1999 |
| Application of Fuzzy Similarity-Based Fractal Dimensions to Characterize Medical Time Series - Manish Sarkar and Tze-Yun Leong - 2001 |
| Application of Kolmogorov Complexity to Inductive Inference with Limited Memory - Andris Ambainis - 1995 |
| An Application of Machine Learning in the Domain of Loan Analysis - José Ferreira, Joaquim Correia, Thomas Jamet and Ernesto Costa - 1993 |
| An application of minimum description length principle to online recognition of handprinted alphanumerals - Q. Gao and M. Li - 1989 |
| Application of Multivariate Maxwellian Mixture Model to Plasma Velocity Distribution - Genta Ueno, Nagatomo Nakamura, Tomoyuki Higuchi, Takashi Tsuchiya, Shinobu Machida and Tohru Araki - 2001 |
| Application of Neural Network Technique to Combustion Spray Dynamics Analysis - Yuji Ikeda and Dariusz Mazurkiewicz - 2001 |
| An Application of the Theory of Computational Complexity to the Study of Inductive Inference - D. Angluin - 1976 |
| Applications of Learning Theorems - V. Faber and J. Mycielski - 1991 |
| Applying formal concepts to learning systems validation - Volker Dötsch, Gunter Grieser and Steffen Lange - 2000 |
| Applying grammatical inference in learning a language model for oral dialogue - Jacques Chodorowski and Laurent Miclet - 1998 |
| Applying the Bayesian Evidence Framework to nu-Support Vector Regression - Martin H. Law and James T. Kwok - 2001 |
| Applying the multiple cause mixture model to text categorization - M. Sahami, M. Hearst and E. Saund - 1996 |
| Applying the weak learning framework to understand and improve C4.5 - Tom Dietterich, Michael Kearns and Yishay Mansour - 1996 |
| Applying Valiant's Learning Framework to AI Concept Learning Problems - D. Haussler - 1987 |
| Applying winnow to context-sensitive spelli ng correction - Andrew R. Golding and Dan Roth - 1996 |
| An apprentice learning model - Stephen S. Kwek - 1999 |
| An Approach to Data Reduction and Clustering with Theoretical Guarantees - Partha Niyogi and Narendra Karmarkar - 2000 |
| The Appropriateness of Predicate Invention as Bias Shift Operation in ILP - Irene Stahl - 1995 |
| Approximate algorithms for neural-Bayesian approaches - Tom Heskes, Bart Bakker and Bert Kappen - 2002 |
| Approximate Dimension Equalization in Vector-based Information Retrieval - Fan Jiang and Michael L. Littman - 2000 |
| Approximate ILP Rules by Backpropagation Neural Network: A Result on Thai Character Recognition - Boonserm Kijsirikul and Sukree Sinthupinyo - 1999 |
| Approximate Inference and Scientific Method - M. A. Fulk and S. Jain - 1994 |
| Approximate language identification - R. M. Wharton - 1974 |
| Approximate learning of random subsequential transducers - Antonio Castellanos - 1998 |
| Approximate Match of Rules Using Backpropagation Neural Networks - Boonserm Kijsirikul, Sukree Sinthupinyo and Kongsak Chongkasemwongse - 2001 |
| Approximate methods for sequential decision making using expert advice - T. H. Chung - 1994 |
| Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms - Thomas G. Dietterich - 1998 |
| Approximate testing and its relationship to learning - Kathleen Romanik - 1997 |
| Approximate testing and learnability - K. Romanik - 1992 |
| Approximating hyper-rectangles: Learning and pseudorandom sets - Peter Auer, Philip M. Long and Aravind Srinivasan - 1998 |
| Approximating value trees in structured dynamic programming - Craig Boutilier and Richard Dearden - 1996 |
| Approximation algorithms for clustering problems - David B. Shmoys - 1999 |
| Approximation and estimation bounds for artificial neural networks - Andrew R. Barron - 1994 |
| Approximation and Learning of Convex Superpositions - Leonid Gurvits and Pascal Koiran - 1997 |
| Approximation by Superpositions of a Sigmoidal Function - G. Cybenko - 1989 |
| Approximation of Boolean functions by sigmoidal networks: Part I: XOR and other two-variable functions - E. K. Blum - February 1989 |
| Approximation via value unification - Paul E. Utgoff and David J. Stracuzzi - 1999 |
| ARACHNID: Adaptive retrieval agents choosing heuristic neighborhoods for information discovery - Filippo Menczer - 1997 |
| Are Case-Based Reasoning and Dissimilarity-Based Classification Two Sides of the Same Coin? - Petra Perner - 2001 |
| Asking questions to minimize errors - Nader H. Bshouty, Sally A. Goldman, Thomas R. Hancock and Sleiman Matar - 1996 |
| Aspects of complexity of conservative probabilistic learning - Léa Meyer - 1998 |
| Aspects of complexity of probabilistic learning under monotonicity constraints - Léa Meyer - 2001 |
| Assistant 86: A Knowledge-Elicitation Tool for Sophisticated Users - B I. K. Cestnik and I. Bratko - May 1987 |
| Associative methods in reinforcement learning: an empirical study - Leslie Pack Kaelbling - 1994 |
| Associative Reinforcement Learning: A Generate and Test Algorithm - Leslie Pack Kaelbling - 1994 |
| Associative Reinforcement Learning: Functions in k-DNF - Leslie Pack Kaelbling - 1994 |
| Associative reinforcement learning using linear probabilistic concepts - Naoki Abe and Philip M. Long - 1999 |
| Asymmetric Co-evolution for Imperfect-Information Zero-Sum Games - Ole Martin Halck and Fredrik A. Dahl - 2000 |
| Asymmetric Team Learning - Kalvis Aps\=ıtis, Rīsiņš Freivalds and Carl H. Smith - 1997 |
| Asymptotic Optimality of Transductive Confidence Machine - Vladimir Vovk - 2002 |
| The Asymptotic Theory of Extreme Order Statistics - Janos Galambos - 1987 |
| Asynchronous Stochastic Approximation and Q-Learning - John N. Tsitsiklis - 1994 |
| Attribute dependencies, understandability and split selection in tree based models - Marko Robnik-ťikonja and Igor Kononenko - 1999 |
| Attribute-efficient learning in query and mistake-bound models - Nader Bshouty and Lisa Hellerstein - 1998 |
| Attribute-efficient learning in query and mistake-bound models - Nader H. Bshouty and Lisa Hellerstein - 1996 |
| Automata techniques for query inference machines - William Gasarch and Geoffrey R. Hird - 2002 |
| Automated Knowledge Acquisition for Strategic Knowledge - Thomas R. Gruber - 1989 |
| Automated Refinement of First-Order Horn-Clause Domain Theories - Bradley L. Richards and Raymond J. Mooney - 1995 |
| Automated Support for Building and Extending Expert Models - Mark A. Musen - 1989 |
| Automatically Extracting Features for Concept Learning from the Web - William W. Cohen - 2000 |
| An automatic construction of LISP programs by transformations of functions synthesized from their input-output behavior - J. P. Jouannaud and Y. Kodratoff - 1980 |
| Automatic Detection of Geomagnetic Jerks by Applying a Statistical Time Series Model to Geomagnetic Monthly Means - Hiromichi Nagao, Tomoyuki Higuchi, Toshihiko Iyemori and Tohru Araki - 2001 |
| Automatic Discovery Heuristics for Nondeterministic Programs From Sample Execution Traces - S. J. Stolfo - 1979 |
| Automatic discovery of heuristics for nondeterministic programs - S. J. Stolfo and M. C. Harrison - 1979 |
| Automatic Discovery of Subgoals in Reinforcement Learning using Diverse Density - Amy McGovern and Andrew G. Barto - 2001 |
| Automatic Identification of Mathematical Concepts - Simon Colton, Alan Bundy and Toby Walsh - 2000 |
| Automatic Indentification of Diatoms Using Decision Forests - Stefan Fischer and Horst Bunke - 2001 |
| Automatic learning, rule extraction and generalization - J. Denker, D. Schwartz, B. Wittner, S. Solla, R. Howard, L. Jackel and J. Hopfield - 1987 |
| Automatic Methods of Inductive Inference - G. D. Plotkin - 1971 |
| Automatic parameter selection by minimizing estimated error - Ron Kohavi and George H. John - 1995 |
| Automatic Pattern Recognition: A Study of the Probability of Error - L. Devroye - July 1988 |
| Automatic Programming of Behavior-Based Robots using Reinforcement Learning - S. Mahadevan and J. Connell - December 1990 |
| Automatic program synthesis for example problems - L. Siklossy and D. Sykes - 1975 |
| Automatic rule acquisition for spelling correction - Lidia Mangu and Eric Brill - 1997 |
| Automatic segmentation of continuous trajectories with invariance to nonlinear warpings of time - Lawrence K. Saul - 1998 |
| Automatic selection of split criterion during tree growing based on node location - Carla E. Brodley - 1995 |
| Automatic speaker recognition: an application of machine learning - Brett Squires and Claude Sammut - 1995 |
| Automatic Synthesis of Programs and Inductive Inference of Functions - K. Jantke - 1979 |
| Automation, using LISP, of inductive inference on sequences - M. Pivar and M. Finkelstein - 1964 |
| Average case analysis of a learning algorithm for mu-DNF expressions - Mostefa Golea - 1995 |
| Average-Case Analysis of Classification Algorithms for Boolean Functions and Decision Trees - Tobias Scheffer - 2000 |
| Average case analysis of k-CNF and k-DNF learning algorithms - Daniel S. Hirschberg, Michael J. Pazzani and Kamal M. Ali - 1994 |
| Average-Case Analysis of Pattern Language Learning Algorithms - Thomas Zeugmann - 1994 |
| Average case analysis of the clipped Hebb rule for nonoverlapping perceptron networks - M. Golea and M. Marchand - 1993 |
| Average-Case Complexity of Learning Polynomials - Frank Stephan and Thomas Zeugmann - 2000 |
| An Average-Case Optimal One-Variable Pattern Language Learner - Rüdiger Reischuk and Thomas Zeugmann - 2000 |
| Average-Reward Reinforcement Learning for Variance Penalized Markov Decision Problems - Makoto Sato and Shigenobu Kobayashi - 2001 |
| Average Reward Reinforcement Learning: Foundations, Algorithms, and Empirical Results - Sridhar Mahadevan - 1996 |
| Averaging Expert Predictions - Jyrki Kivinen and Manfred K. Warmuth - 1999 |
| Avoiding Coding Tricks by Hyperrobust Learning - Matthias Ott and Frank Stephan - 1999 |
| Avoiding coding tricks by hyperrobust learning - Matthias Ott and Frank Stephan - 2002 |
| An Axiomatic Approach to Feature Term Generalization - Hassan A\"ıt-Kaci and Yutaka Sasaki - 2001 |
