 | PAB-decisions for Boolean and real-valued features - S. Anoulova, P. Fischer, S. Pölt and H. U. Simon - 1992 |
| PAC Adaptive Control of Linear Systems - Claude-Nicolas Fiechter - 1997 |
| A `PAC' Algorithm for Making Feature Maps - Philip Laird and Evan Gamble - 1991 |
| PAC Analogues of Perceptron and Winnow via Boosting the Margin - Rocco A. Servedio - 2000 |
| PAC Analogues of Perceptron and Winnow Via Boosting the Margin - R. Servedio - 2002 |
| A PAC Analysis of a Bayesian Estimator - John Shawe-Taylor and Robert C. Williamson - 1997 |
| PAC-Bayesian model averaging - David A. McAllester - 1999 |
| PAC Bounds for Multi-armed Bandit and Markov Decision Processes - Eyal Even-Dar, Shie Mannor and Yishay Mansour - 2002 |
| Packet Analysis in Congested Networks - Masaki Fukushima and Shigeki Goto - 2001 |
| PAC-learnability of constrained nonrecursive logic programs - Sašo Džeroski, Stephen Muggleton and Stuart Russell - 1995 |
| PAC-learnability of determinate logic programs - S. Dzeroski, S. Muggleton and S. Russell - 1992 |
| Pac-Learning a Restricted Class of Recursive Logic Programs - William Cohen - 1993 |
| PAC learning axis-aligned rectangles with respect to product distributions from multiple-instance examples - Philip M. Long and Lei Tan - 1998 |
| PAC learning from general examples - Paul Fischer, Klaus-Uwe Höffgen and Hanno Lefmann - 1997 |
| PAC Learning from Positive Statistical Queries - François Denis - 1998 |
| PAC Learning Intersections of Halfspaces with Membership Queries - Stephen Kwek and Leonard Pitt - 1998 |
| PAC learning of concept classes through the boundaries of their items - B. Apolloni and S. Chiaravalli - 1997 |
| PAC Learning of One-Dimensional Patterns - Paul W. Goldberg, Sally A. Goldman and Stephen D. Scott - 1996 |
| Pac-Learning Recursive Logic Programs: Efficient Algorithms - William W. Cohen - 1995 |
| Pac-Learning Recursive Logic Programs: Negative Results - William W. Cohen - 1995 |
| PAC Learning under Helpful Distributions - François Denis and Rémi Gilleron - 2001 |
| PAC learning under helpful distributions - François Denis and Rémi Gilleron - 1997 |
| PAC learning using Nadaraya-Watson estimator based on orthonormal systems - Hongzhu Qiao, Nageswara S. V. Rao and V. Protopopescu - 1997 |
| PAC learning with constant-partition classification noise and applications to decision tree induction - Scott Decatur - 1997 |
| PAC learning with generalized samples and an application to stochastic geometry - S. R. Kulkarni, S. K. Mitter, J. N. Tsitsiklis and O. Zeitouni - 1992 |
| PAC learning with irrelevant attributes - Aditi Dhagat and Lisa Hellerstein - 1994 |
| PAC Learning with Nasty Noise - Nader H. Bshouty, Nadav Eiron and Eyal Kushilevitz - 1999 |
| PAC Learning with Nasty Noise - Nader H. Bshouty, Nadav Eiron and Eyal Kushilevitz - 2002 |
| PAC learning with simple examples - F. Denis, C. D'Halluin and R. Gilleron - 1996 |
| PAC-like upper bounds for the sample complexity of leave-one-out cross-validation - Sean B. Holden - 1996 |
| PACS, simple-PAC and query learning - Jorge Castro and David Guijarro - 2000 |
| Pairwise Comparison of Hypotheses in Evolutionary Learning - Krzysztof Krawiec - 2001 |
| PAL: A Pattern and dash;Based First and dash;Order Inductive System - Eduardo F. Morales - 1997 |
| PALO: A probabilistic hill-climbing algorithm - Russell Greiner - July 1996 |
| Paradigms in Measure Theoretic Learning and in Informant Learning - Franco Montagna and Giulia Simi - 1999 |
| Parallel Attribute-Efficient Learning of Monotone Boolean Functions - Peter Damaschke - 2000 |
| Parallel Distributed Processing - Explorations in the Microstructure of Cognition - David E. Rumelhart, James L. McClelland and the PDP Research Group - 1986 |
| Parallel Distributed Processing (Volume I: Foundations) - D. E. Rumelhart and J. L. McClelland - 1986 |
| Parallel Networks that Learn to Pronounce English Text - T. J. Sejnowski and C. R. Rosenberg - February 1987 |
| A Parallel Network that Learns to Play Backgammon - G. Tesauro and T. J. Sejnowski - 1989 |
| Parameter Estimation in Stochastic Logic Programs - James Cussens - 2001 |
| Parameterized learning complexity - R. Downey, P. Evans and M. Fellows - 1993 |
| A parametrization scheme for classifying models of learnability - S. Ben-David, G. M. Benedek and Y. Mansour - 1989 |
| Partial Linear Trees - Lu\'ıs Torgo - 2000 |
| Partially Supervised Learning for Nearest Neighbor Classifiers - Hiroyuki Matsunaga and Kiichi Urahama - 1996 |
| Partially Supervised Text Classification: Combining Labeled and Unlabeled Documents Using an EM-like Scheme - Carsten Lanquillon - 2000 |
| Partial Occam's razor and its applications - Carlos Domingo, Tatsuie Tsukiji and Osamu Watanabe - 1997 |
| The Parti-game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-spaces - Andrew W. Moore and Christopher G. Atkeson - 1995 |
| Passive distance learning for robot navigation - Sven Koenig and Reid G. Simmons - 1996 |
| Pasting Small Votes for Classification in Large Databases and On-Line - Leo Breiman - 1999 |
| Path Kernels and Multiplicative Updates - Eiji Takimoto and Manfred K. Warmuth - 2002 |
| A Pathology of Bottom-Up Hill-Climbing in Inductive Rule Learning - Johannes Fürnkranz - 2002 |
| Pattern Classification and Scene Analysis - R. O. Duda and P. E. Hart - 1973 |
| A Pattern Classification Approach to Evaluation Function Learning - K. Lee and S. Mahajan - August 1988 |
| Pattern discovery in biosequences - Alvis Brāzma, Inge Jonassen, Jaak Vilo and Esko Ukkonen - 1998 |
| Pattern Inference - T. Shinohara and S. Arikawa - 1995 |
| Pattern languages are not learnable - R. E. Schapire - 1990 |
| Pattern Recognition and Density Estimation under the General i.i.d Assumption - Ilia Nouretdinov, Volodya Vovk, Michael Vyugin and Alex Gammerman - 2001 |
| Pattern Recognition and Valiant's Learning Framework - L. Saitta and F. Bergadano - February 1993 |
| Pattern recognition as rule guided inductive inference - R. Michalski - 1980 |
| PCA-Based Model Selection and Fitting for Linear Manifolds - Atsushi Imiya and Hisashi Ootani - 2001 |
| The Perceptron Algorithm is Fast for Nonmalicious Distributions - E. B. Baum - 1990 |
| The perceptron algorithm vs. Winnow: linear vs. logarithmic mistake bounds when few input variables are relevant - Jyrki Kivinen and Manfred K. Warmuth - 1995 |
| The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain - F. Rosenblatt - 1958 |
| Perceptrons - M. Minsky and S. Papert - 1969 |
| A Perceptual Criterion for Visually Controlling Learning - Masaki Suwa and Hiroshi Motoda - 1993 |
| Performance bounds for nonlinear time series prediction - Ron Meir - 1997 |
| A performance evaluation of automatic survey classifiers - P. Viechnicki - 1998 |
| Performance Guarantees for Hierarchical Clustering - Sanjoy Dasgupta - 2002 |
| Performance Improvement of Robot Continuous-Path Operation through Iterative Learning Using Neural Networks - Peter C. Y. Chen, James K. Mills and Kenneth C. Smith - 1996 |
| Performance of a Stochastic Learning Chip - J. Alspector and R. B. Allen - 1989 |
| Periodicity in generations of automata - J. Case - 1974 |
| Permutations and Control Sets for Learning Non-regular Language Families - Henning Fernau and Jose M. Sempere: - 2000 |
| Pessimistic decision tree pruning based on tree size - Yishay Mansour - 1997 |
| Pharmacophore discovery using the inductive logic programming system PROG0L - Paul Finn, Stephen Muggleton, David Page and Ashwin Srinivasan - 1998 |
| Phase Transitions in Relational Learning - Attilio Giordana and Lorenza Saitta - 2000 |
| Phoneme Recognition Using Time-Delay Neural Networks - A. Waibel, T. Hanazawa, G. Ginton and K. Shikano - 1987 |
| A Physiological Basis for a Theory of Synapse Modification - M. F. Bear, L. N. Cooper and F. F. Ebner - July 3 1987 |
| Piecemeal Graph Exploration by a Mobile Robot - Baruch Awerbuch, Margrit Betke, Ronald L. Rivest and Mona Singh - 1999 |
| Piecemeal Learning of an Unknown Environment - Margrit Betke, Ronald L. Rivest and Mona Singh - 1995 |
| Planning and learning in permutation groups - A. Fiat, S. Moses, A. Shamir, I. Shimshoni and G. Tardos - 1989 |
| Planning with abstraction based on partial predicate mappings - Yoshiaki Okubo and Makoto Haraguchi - 1993 |
| Plausible Justification Trees: A Framework for Deep and Dynamic Integration of Learning Strategies - Gheorghe Tecuci - 1993 |
| Playing the matching-shoulders lob-pass game with logarithmic regret - J. Kilian, K. J. Lang and B. A. Pearlmutter - 1994 |
| Policy invariance under reward transformations: theory and application to reward shaping - Andrew Y. Ng, Daishi Harada and Stuart Russell - 1999 |
| Polylogarithmic-overhead piecemeal graph exploration - Baruch Awerbuch and Stephen G. Kobourov - 1998 |
| A Polynomial Approach to the Constructive Induction of Structural Knowledge - Jörg-Uwe Kietz and Katharina Morik - 1994 |
| Polynomial Bounds for VC Dimension of Sigmoidal and General Pfaffian Neural Networks - Marek Karpinski and Angus Macintyre - 1997 |
| Polynomial bounds for VC dimension of sigmoidal neural networks - Marek Karpinski and Angus Macintyre - 1995 |
| Polynomial Learnability as a Formal Model of Natural Language Acquisition - Naoki Abe - 1989 |
| Polynomial learnability of linear threshold approximations - T. Bylander - 1993 |
| Polynomial learnability of probabilistic concepts with respect to the Kullback-Leibler divergence - N. Abe, J. Takeuchi and M. K. Warmuth - 1991 |
| Polynomial Learnability of Stochastic Rules with Respect to the KL-Divergence and Quadratic Distance - Naoki Abe, Jun-ichi Takeuchi and Manfred K. Warmuth - 2001 |
| Polynomial learning of semilinear sets - N. Abe - 1989 |
| Polynomially Sized Boolean Circuits are not Learnable - D. Beaver - December 1987 |
| Polynomially Sparse Variations and Reducibility among prediction problems - N. Abe and O. Watanabe - July 1992 |
| A polynomial time algorithm for finding finite unions of tree pattern languages - H. Arimura, T. Shinohara and S. Otsuki - 1993 |
| A polynomial-time algorithm for learning k-variable pattern languages from examples - M. Kearns and L. Pitt - 1989 |
| A Polynomial-Time Algorithm for Learning Noisy Linear Threshold Functions - Avrim Blum, Alan M. Frieze, Ravi Kannan and Santosh Vempala - 1998 |
| Polynomial time algorithms for learning neural nets - E. B. Baum - 1990 |
| A polynomial time algorithm that learns two hidden net units - E. Baum - 1990 |
| A Polynomial Time Approximation Scheme for Inferring Evolutionary Trees from Quartet Topologies and Its Application - Tao Jiang, Paul Kearney and Ming Li - 2000 |
| Polynomial-time identification of very regular languages in the limit - N. Tanida and T. Yokomori - 1992 |
| A polynomial time incremental algorithm for learning DFA - Rajes Parekh, Codrin Nichitiu and Vasant Honavar - 1998 |
| Polynomial Time Inductive Inference of Ordered Tree Patterns with Internal Structured Variables from Positive Data - Yusuke Suzuki, Ryuta Akanuma, Takayoshi Shoudai, Tetsuhiro Miyahara and Tomoyuki Uchida - 2002 |
| Polynomial time inductive inference of regular term tree languages from positive data - Satoshi Matsumoto, Yukiko Hayashi and Takayoshi Shoudai - 1997 |
| Polynomial-time inference of all valid implications for Horn and related formulae - E. Boros, Y. Crama and P. L. Hammer - 1990 |
| Polynomial-time inference of arbitrary pattern languages - S. Lange and R. Wiehagen - 1991 |
| Polynomial time inference of a subclass of context-free transformations - H. Arimura, H. Ishizaka and T. Shinohara - 1992 |
| Polynomial Time Inference of Extended Regular Pattern Languages - T. Shinohara - 1982 |
| Polynomial-time inference of general pattern languages - K. P. Jantke - 1984 |
| Polynomial-Time Inference of Paralleled Even Monogenic Pure Context-Free Languages - Noriyuki Tanida - 1998 |
| Polynomial time inference of pattern languages and its applications - T. Shinohara - 1982 |
| Polynomial time inference of unions of tree pattern languages - H. Arimura, T. Shinohara and S. Otsuki - 1992 |
| Polynomial-time learnability of logic programs with local variables from entailment - M. R. K. Krishna Rao and A. Sattar - 2001 |
| Polynomial time learnability of simple deterministic languages - Hiroki Ishizaka - 1990 |
| Polynomial Time Learnability of Simple Deterministic Languages from MAT and a Representative Sample - Yasuhiro Tajima, Etsuji Tomita and Mitsuo Wakatsuki - 2000 |
| A Polynomial Time Learning Algorithm Simple Deterministic Languages via Membership Queries and a Representative Sample - Yasuhiro Tajima and Etsuji Tomita - 2000 |
| Polynomial-time Learning of Elementary Formal Systems - Satoru Miyano, Ayumi Shinohara and Takeshi Shinohara - 2000 |
| Polynomial-time learning of very simple grammars from positive data - T. Yokomori - 1991 |
| Polynomial time learning with version spaces - Haym Hirsh - 1992 |
| Polynomial-time MAT learning of multilinear logic programs - Kimihito Ito and Akihiro Yamamoto - 1993 |
| Polynomial uniform convergence and polynomial-sample learnability - A. Bertoni, P. Campadelli, A. Morpurgo and S. Panizza - 1992 |
| Positive and Unlabeled Examples Help Learning - Francesco De Comité, François Denis, Remi Gilleron and Fabien Letouzey - 1999 |
| A-posteriori Characterizations in Inductive Inference of Recursive Functions - Thomas Zeugmann - 1983 |
| Potential-Based Algorithms in On-line Prediction and Game Theory - Nicolò Cesa-Bianchi and Gabor Lugosi - 2001 |
| A powerful heuristic for the discovery of complex patterned behavior - Raúl E. Valdés-Pérez and Aurora Pérez - 1994 |
| The Power of Amnesia: Learning Probabilistic Automata with Variable Memory Length - Dana Ron, Yoram Singer and Naftali Tishby - 1996 |
| The Power of Pluralism for Automatic Program Synthesis - Carl H. Smith - 1982 |
| The power of probabilism in popperian FINite learning - R. Daley, B. Kalyanasundaram and M. Velauthapillai - 1994 |
| The power of procrastination in inductive inference: how it depends on used ordinal notations - Andris Ambainis - 1995 |
| The Power of Self-Directed Learning - S. A. Goldman and R. H. Sloan - 1994 |
| The power of team exploration: two robots can learn unlabeled directed graphs - Michael A. Bender and Donna K. Slonim - 1994 |
| The power of vacillation - J. Case - 1988 |
| The Power of Vacillation in Language Learning - John Case - 1999 |
| Practical algorithms for on-line sampling - Carlos Domingo, Ricard Gavaldà and Osamu Watanabe - 1998 |
| A practical approach for evaluating generalization performance - Marjorie Klenin - 1997 |
| Practical Issues in Temporal Difference Learning - Gerald Tesauro - 1992 |
| Practical PAC Learning - Dale Schuurmans and Russell Greiner - 1995 |
| Practical Reinforcement Learning in Continuous Spaces - William D. Smart and Leslie Pack Kaelbling - 2000 |
| The Precision of Query Points as a Resource for Learning Convex Polytopes with Membership Queries - Paul Goldberg and Stephen Kwek - 2000 |
| Predicate invention and utilization - S. Muggleton - 1994 |
| Predicate Invention in ILP - an Overview - Irene Stahl - 1993 |
| Predicate Invention in Inductive Data Engineering - Peter A. Flach - 1993 |
| Predicting {0,1} Functions on Randomly Drawn Points - D. Haussler, N. Littlestone and M. K. Warmuth - 1994 |
| Predicting a binary sequence almost as well as the optimal biased coin - Yoav Freund - 1996 |
| Predicting multiprocessor memory access patterns with learning models - M. F. Sakr, S. P. Levitan, D. M. Chiarulli, B. G. Horne and C. L. Giles - 1997 |
| Predicting Nearly as Well as the Best Pruning Graph - Eiji Takimoto and Manfred K. Warmuth - 2002 |
| Predicting nearly as well as the best pruning of a decision tree - D. P. Helmbold and R. E. Schapire - 1997 |
| Predicting nearly as well as the best pruning of a decision tree through dynamic programming scheme - Eiji Takimoto, Akira Maruoka and Volodya Vovk - 2001 |
| Predicting Nearly as Well as the Best Pruning of a Planar Decision Graph - Eiji Takimoto and Manfred K. Warmuth - 1999 |
| Predicting protein secondary structure using stochastic tree grammars - Naoki Abe and Hiroshi Mamitsuka - 1997 |
| Predicting the Future: A Connectionist Approach - A. Weigend, B. Huberman and D. Rumelhart - 1990 |
| Predicting the Future of Discrete Sequences from Fractal Representations of the Past - Peter Tino and Georg Dorffner - 2001 |
| Predicting the Generalization Performance of Cross Validatory Model Selection Criteria - Tobias Scheffer - 2000 |
| Prediction algorithms and confidence measures based on algorithmic randomness theory - Alex Gammerman and Volodya Vovk - 2002 |
| Prediction and Dimension - Lance Fortnow and Jack H. Lutz - 2002 |
| Prediction and limiting synthesis of recursively enumerable classes of functions - J. M. Barzdin and R. Freivalds - 1974 |
| Prediction, learning, uniform convergence, and scale-sensitive dimensions - Peter L. Bartlett and Philip M. Long - 1998 |
| Prediction Preserving Reducibility - L. Pitt and M. K. Warmuth - December 1990 |
| Predictive Complexity and Information - Michael V. Vyugin and Vladimir V. V'yugin - 2002 |
| Predictive Hebbian learning - Terrence J. Sejnowski, Peter Dayan and P. Read Montague - 1995 |
| Predictive Learning Models for Concept Drift - John Case, Sanjay Jain, Susanne Kaufmann, Arun Sharma and Frank Stephan - 1998 |
| Predictive learning models for concept drift - John Case, Sanjay Jain, Susanne Kaufmann, Arun Sharma and Frank Stephan - 2001 |
| A predictive model for self organizing systems, Part I - G. Pask and H. V. Foerster - 1960 |
| A predictive model for self organizing systems, Part II - G. Pask and H. V. Foerster - 1961 |
| Preface - S. Arikawa and M. M. Richter - 1997 |
| Preface - Osamu Watanabe and Arun Sharma - 2002 |
| A preliminary PAC analysis of theory revision - Raymond J. Mooney - 1995 |
| Preliminary study on program synthesis based on induction and verification - K. Furukawa - 1992 |
| Prequential Analysis, Stochastic Complexity and Bayesian Inference - A. P. Dawid - 1992 |
| Prequential and Cross-Validated Regression Estimation - Dharmendra S. Modha and Elias Masry - 1998 |
| Preservation of predictability under polynomially sparse variations and its applications - N. Abe - 1992 |
| Preventing Overfitting of cross-validation data - Andrew Y. Ng - 1997 |
| A Principal Components Approach to Combining Regression Estimates - Christopher J. Merz and Michael J. Pazzani - 1999 |
| Prioritized Sweeping: Reinforcement Learning With Less Data and Less Time - Andrew W. Moore and Christopher G. Atkeson - 1993 |
| A probabilistic analysis of the Rocchio algorithm with TFIDF for text categorization - Thorsten Joachims - 1997 |
| Probabilistic and team PFIN-type learning: general properties - Andris Ambainis - 1996 |
| A probabilistic approach to feature selection - a filter solution - Huan Liu and Rudy Setiono - 1996 |
| Probabilistic DFA Inference using Kullback-Leibler Divergence and Minimality - Franck Thollard, Pierre Dupont and Colin de la Higuera - 2000 |
| A Probabilistic Framework for SVM Regression and Error Bar Estimation - J. B. Gao, S. R. Gunn, C. J. Harris and M. Brown - 2002 |
| Probabilistic hill-climbing - William W. Cohen, Russell Greiner and Dale Schuurmans - 1994 |
| Probabilistic Hill-Climbing: Theory and Applications - Russell Greiner - June 1992 |
| A Probabilistic Identification Result - Eric McCreath - 2000 |
| Probabilistic Inductive Inference - L. Pitt - 1989 |
| Probabilistic inductive inference: a survey - Andris Ambainis - 2001 |
| Probabilistic Inductive Inference (Ph.D Thesis) - L. Pitt - 1985 |
| Probabilistic instance-based learning - Henry Tirri, Petri Kontkanen and Petri Myllymäki - 1996 |
| Probabilistic k-Testable Tree Languages - Juan Ramón Rico-Juan, Jorge Calera-Rubio and Rafael C. Carrasco - 2000 |
| Probabilistic language learning under monotonicity constraint - Léa Meyer - 1997 |
| Probabilistic limit identification up to 'small' sets - Juris Vīksna - 1996 |
| Probabilistic linear tree - João Gama - 1997 |
| Probabilistic Program Synthesis - K. Podnieks - 1977 |
| Probabilistic self-structuring and learning - David Garvin and Peter Rayner - 1997 |
| Probabilistic synthesis of enumerated classes of functions - K. M. Podnieks - 1975 |
| Probabilistic Versus Deterministic Inductive Inference in Nonstandard Numberings - R. Freivalds, E. B. Kinber and R. Wiehagen - 1988 |
| Probabilistic versus Deterministic Memory Limited Learning - R. Freivalds, E. B. Kinber and C. H. Smith - 1995 |
| A Probability Analysis on the Value of Unlabeled Data for Classification Problems - Tong Zhang and Frank J. Oles - 2000 |
| Probability and confirmation - H. Putnam - 1975 |
| Probability and Measure - Patrick Billingsley - 1986 |
| Probability and Plurality for Aggregations of Learning Machines - L. Pitt and C. Smith - 1988 |
| Probability density estimation and local basis function neural networks - Padhraic Smyth - 1994 |
| Probability inequalities for sums of bounded random variables - W. Hoeffding - March 1963 |
| Probability is more powerful than team for language identification from positive data - S. Jain and A. Sharma - 1993 |
| Probability Matching, the Magnitude of Reinforcement, and Classifier System Bidding - David E. Goldberg - 1990 |
| Probability theory for the Brier game - V. Vovk - 1997 |
| Probability theory for the Brier game - V. Vovk - 2001 |
| Probably almost Bayes decisions - P. Fischer, S. Pölt and H. U. Simon - 1991 |
| Probably Almost Bayes Decisions - Svetlana Anoulova, Paul Fischer, Stefan Pölt and Hans Ulrich Simon - August 1996 |
| Probably Almost Discriminative Learning - Kenji Yamanishi - 1995 |
| Probably Approximate Learning of Sets and Functions - B. K. Natarajan - April 1991 |
| Probably-Approximate Learning over Classes of Distributions - B. K. Natarajan - 1989 |
| Probably Approximately Correct Learning - D. Haussler - 1990 |
| The Probably Approximately Correct (PAC) and Other Learning Models - D. Haussler and M. K. Warmuth - 1993 |
| Probably Approximately Optimal Derivation Strategies - Russell Greiner and Pekka Orponen - April 1991 |
| Probably Approximately Optimal Satisficing Strategies - Russell Greiner and Pekka Orponen - April 1996 |
| Problem Decomposition for Behavioural Cloning - Dorian Suc and Ivan Bratko - 2000 |
| The Problem of Expensive Chunks and its Solution by Restricting Expressiveness - Milind Tambe, Allen Newell and Paul S. Rosenbloom - 1990 |
| Problems in Inductive Inference - J. Case - 1982 |
| Problems of computational and information complexity in machine vision and learning - S. R. Kulkarni - 1991 |
| The problem with noise and small disjuncts - Gary M. Weiss and Haym Hirsh - 1998 |
| A procedure for unsupervised lexicon learning - Anand Venkataraman - 2001 |
| Proceedings of the Eighth Annual Conference on Computational Learning Theory - Wolfgang Maass - 1995 |
| Proceedings of the Eleventh Annual Conference on Computational Learning Theory - Peter Bartlett and Yishay Mansour - 1998 |
| Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory - David Haussler - 1992 |
| Proceedings of the First Annual Workshop on Computational Learning Theory - D. Haussler and L. Pitt - 1988 |
| Proceedings of the First International Workshop on Algorithmic Learning Theory - S. Arikawa and S. Goto and S. Ohsuga and T. Yokomori - 1990 |
| Proceedings of the Fourth Annual Workshop on Computational Learning Theory - Leslie G. Valiant and Manfred K. Warmuth - 1991 |
| Proceedings of the Ninth Annual Conference on Computational Learning Theory - Avrim Blum and Michael Kearns - 1996 |
| Proceedings of the Second Annual Workshop on Computational Learning Theory - Ronald Rivest and David Haussler and Manfred K. Warmuth - 1989 |
| Proceedings of the Second International Workshop on Algorithmic Learning Theory - S. Arikawa and A. Maruoka and T. Sato - 1991 |
| Proceedings of the Seventh Annual ACM Conference on Computational Learning Theory - Manfred Warmuth - 1994 |
| Proceedings of the Sixth Annual ACM Conference on Computational Learning Theory - Lenny Pitt - 1993 |
| Proceedings of the Tenth Annual Conference on Computational Learning Theory - Yaov Freund and Robert Shapire - 1997 |
| Proceedings of the Third Annual Workshop on Computational Learning Theory - Mark A. Fulk and John Case - 1990 |
| Proceedings of the Thirteenth Annual Conference on Computational Learning Theory - N. Cesa-Bianchi and S. Goldman - 2000 |
| Proceedings of the Twelfth Annual Conference on Computational Learning Theory - Shai Ben-David and Phil Long - 1999 |
| A process-oriented heuristic for model selection - Pedro Domingos - 1998 |
| Prognostication of automata and functions - J. M. Barzdin - 1971 |
| Program Construction From Examples - P. D. Summers - 1976 |
| Program Error Detection/Correction: Turning PAC Learning into PERFECT Learning - Manuel Blum - 1997 |
| Program Size and Teams for Computational Learning - A. Sharma - 1990 |
| Program size in restricted programming languages - A. Meyer - 1972 |
| Program Size Restrictions in Computational Learning - Sanjay Jain and Arun Sharma - 1994 |
| Program Size Restrictions in Inductive Learning - S. Jain and A. Sharma - 1990 |
| Program Synthesis in the Presence of Infinite Number of Inaccuracies - Sanjay Jain - 1996 |
| Projection Learning - Leslie G. Valiant - 1999 |
| Projection Pursuit - Peter J. Huber - 1985 |
| Projection pursuit learning: some theoretical issues - Ying Zhao and Christopher G. Atkeson - 1994 |
| Projection Pursuit Regression - J. H. Friedman and W. Stuetzle - December 1981 |
| Proper learning algorithm for functions of k terms under smooth distributions - Y. Sakai, E. Takimoto and A. Maruoka - 1999 |
| Properties of Language Classes with Finite Elasticity - Takashi Moriyama and Masako Sato - 1993 |
| Property testing and its connection to learning and approximation - Oded Goldreich, Shari Goldwasser and Dana Ron - 1998 |
| Proportional k-Interval Discretization for Naive-Bayes Classifiers - Ying Yang and Geoffrey I. Webb - 2001 |
| Protein folding: symbolic refinement competes with neural networks - Susan Craw and Paul Hutton - 1995 |
| Protein secondary structure prediction based on stochastic-rule learning - Hiroshi Mamitsuka and Kenji Yamanishi - 1993 |
| Prototype and feature selection by sampling and random mutation hill climbing algorithms - David B. Skalak - 1994 |
| Prototype Nonintrusive Appliance Load Monitor - G. W. Hart - September 1985 |
| Prototype selection using competitive learning - Michael Lemmon - 1994 |
| Proving based on similarity - K. Fujita and M. Harao - 1992 |
| Prudence and Other Conditions on Formal Language Learning - M. A. Fulk - 1990 |
| Prudence in Language Learning - S. A. Kurtz and J. S. Royer - 1988 |
| Prudence in Vacillatory Language Identification - S. Jain and A. Sharma - 1995 |
| Pruning adaptive boosting - Dragos D. Margineantu and Thomas G. Dietterich - 1997 |
| Pruning Algorithms for Rule Learning - Fürnkranz Johannes - 1997 |
| Pseudo-convergent Q-Learning by Competitive Pricebots - Jeffrey O. Kephart and Gerald J. Tesauro - 2000 |
| P-sufficient statistics for PAC learning k-term-DNF formulas through enumeration - B. Apolloni and C. Gentile - 2000 |
| A PTAS for Clustering in Metric Spaces - Piotr Indyk - 1999 |
| Purposive Behavior Acquisition for a Real Robot by Vision-Based Reinforcement Learning - Minoru Asada, Shoichi Noda, Sukoya Tawaratsumida and Koh Hosoda - 1996 |
