 | SAMIA: A Bottom-Up Learning Method Using a Simulated Annealing Algorithm - Pierre Brézelle and Henri Soldano - 1993 |
 | The sample complexity of learning fixed-structure Bayesian networks - Sanjoy Dasgupta - 1997 |
 | Sample Complexity of Model-Based Search - Christopher D. Rosin - 2000 |
 | Sample compression, learnability, and the Vapnik-Chervonenkis dimension - Manfred Warmuth - 1997 |
 | Sample compression, learnability, and the Vapnik-Chervonenkis dimension - Sally Floyd and Manfred Warmuth - 1995 |
 | Sample-efficient strategies for learning in the presence of noise - Nicolò Cesa-Bianchi, Eli Dichterman, Paul Fischer, Eli Shamir and Hans Ulrich Simon - 1999 |
 | Sample sizes for sigmoidal neural networks - John Shawe-Taylor - 1995 |
 | A sane algorithm for the synthesis of LISP functions from example problems - Y. Kodratoff and J. Fargues - 1978 |
 | Saving the Phenomenon: Requirements that Inductive Inference Machines not Contradict Known Data - M. A. Fulk - 1988 |
 | Scalability Issues in Inductive Logic Programming - Stefan Wrobel - 1998 |
 | Scalability, Search, and Sampling: From Smart Algorithms to Active Discovery - Stefan Wrobel - 2001 |
 | Scalable and Comprehensible Visualization for Discovery of Knowledge from the Internet - Etsuya Shibayama, Masashi Toyoda, Jun Yabe and Shin Takahashi - 2001 |
 | Scale-sensitive dimensions, uniform convergence, and learnability - Noga Alon, Shai Ben-David, Nicolò Cesa-Bianchi and David Haussler - 1997 |
 | Scaling Reinforcement Learning toward RoboCup Soccer - Peter Stone and Richard S. Sutton - 2001 |
 | Scaling relationships in back-propagation learning - G. Tesauro and B. Janssens - 1988 |
 | Scaling relationships in back-propagation learning: dependence on training set size - G. Tesauro - 1987 |
 | Scaling to domains with irrelevant features - Patrick Langley and Stephanie Sage - 1997 |
 | Scaling up average reward reinforcement learning by approximating the domain models and the value function - Prasad Tadepalli and DoKyeong Ok - 1996 |
 | Scaling Up Inductive Learning with Massive Parallelism - Foster John Provost and John M. Aronis - 1996 |
 | A schema for using multiple knowledge - Matjaž Gams, Marko Bohanec and Bojan Cestnik - 1994 |
 | The Schema Mechanism: A Conception of Constructivist Intelligence - G. L. Drescher - February 1985 |
 | Scientific discovery based on belief revision - Eric Martin and Daniel N. Osherson - December 1997 |
 | Searching for Mutual Exclusion Algorithms Using BDDs - Koichi Takahashi and Masami Hagiya - 2001 |
 | Searching for Representations to Improve Protein Sequence Fold-Class Prediction - Thomas R. Ioerger, Larry A. Rendell and Shankar Subramaniam - 1995 |
 | Searching for structure in multiple streams of data - Tim Oates and Paul R. Cohen - 1996 |
 | Searching in an Unknown Environment: An Optimal Randomized Algorithm for the Cow-Path Problem - M. Kao, J. H. Reif and S. R. Tate - January 1993 |
 | Searching in the presence of linearly bounded errors - J. A. Aslam and A. Dhagat - 1991 |
 | Second Difference Method Reinforced by Grouping: A New Tool for Assistance in Assignment of Complex Molecular Spectra - Takehiko Tanaka - 2001 |
 | Second Order Features for Maximising Text Classification Performance - Bhavani Raskutti, Herman Ferrá and Adam Kowalczyk - 2001 |
 | A Second-Order Perceptron Algorithm - Nicolò Cesa-Bianchi, Alex Conconi and Claudio Gentile - 2002 |
 | Second tier for decision trees - Miroslav Kubat - 1996 |
 | Selecting a Classification Method by Cross-Validation - Cullen Schaffer - 1993 |
 | Selecting Examples for Partial Memory Learning - Marcus A. Maloof and Ryszard S. Michalski - 2000 |
 | Selection criteria for word trigger pairs in language modelling - Christoph Tillmann and Hermann Ney - 1996 |
 | Selection of Support Vector Kernel Parameters for Improved Generalization - Loo-Nin Teow and Kia-Fock Loe - 2000 |
 | Selective reformulation of examples in concept learning - Jean-Daniel Zucker and Jean-Gabriel Ganascia - 1994 |
 | Selective sampling using the query by committee algorithm - Yoav Freund, H. Sebastian Seung, Eli Shamir and Naftali Tishby - 1997 |
 | Selective Voting for Perceptron-like Online Learning - Yi Li - 2000 |
 | Self bounding learning algorithms - Yoav Freund - 1998 |
 | Self-Directed Learning and Its Relation to the VC-Dimension and to Teacher-Directed Learning - Shai Ben-David and Nadav Eiron - 1998 |
 | Self-Duality of Bounded Monotone Boolean Functions and Related Problems - Daya Ram Gaur and Ramesh Krishnamurti - 2000 |
 | Self-improving factory simulation using continuous-time average-reward reinforcement learning - Sridhar Mahadevan, Nicholas Marchalleck, Tapas K. Das and Abhijit Gosavi - 1997 |
 | Self-Improving Reactive Agents Based On Reinforcement Learning, Planning and Teaching - Long-ji Lin - 1992 |
 | Self-improving reactive agents: case studies of Reinforcement Learning Frameworks - L. Lin - August 1990 |
 | Self-learning reaching motion of a multi-joint arm using a trial-and-error heuristic and a neural network - K. Amakawa - 1991 |
 | Self-Optimizing and Pareto-Optimal Policies in General Environments Based on Bayes-Mixtures - Marcus Hutter - 2002 |
 | Semi-supervised learning - L. Pitt and R. Board - 1987 |
 | Semi-Supervised Learning - R. A. Board and L. Pitt - 1989 |
 | Sensitive discount optimality: unifying discounted and average reward reinforcement learning - Sridhar Mahadevan - 1996 |
 | Sensitivity constraints in learning - Scott H. Clearwater and Yongwon Lee - 1994 |
 | Separating distribution-free and mistake-bound learning models over the Boolean domain - A. Blum - 1990 |
 | Separating PAC and Mistake-Bound Learning Models over the Boolean Domain - A. Blum - 1990 |
 | A Sequential Approximation Bound for Some Sample-Dependent Convex Optimization Problems with Applications in Learning - Tong Zhang - 2001 |
 | Sequential PAC learning - Dale Schuurmans and Russell Greiner - 1995 |
 | Sequential prediction of individual sequences under general loss functions - D. Haussler, J. Kivinen and M. K. Warmuth - 1998 |
 | Sequential Sampling Techniques for Algorithmic Learning Theory - Osamu Watanabe - 2000 |
 | Set-driven and rearrangement-independent learning of recursive languages - S. Lange and T. Zeugmann - 1996 |
 | Shaping in Reinforcement Learning by Changing the Physics of the Problem - Jette Randløv - 2000 |
 | Sharper Bounds for the Hardness of Prototype and Feature Selection - Richard Nock and Marc Sebban - 2000 |
 | Shifting inductive bias with success-story algorithm, adaptive Levin search, and incremental self-improvement - Jürgen Schmidhuber, Jieyu Zhao and Marco Wiering - 1997 |
 | Shifting Vocabulary Bias in Speedup Learning - Devika Subramanian - 1995 |
 | Shift of Bias for Inductive Concept Learning - P. Utgoff - 1987 |
 | Short-Term Profiling for a Case-Based Reasoning - Esma A\"ımeur and Mathieu Vézeau - 2000 |
 | SIA: A Supervised Inductive Algorithm with Genetic Search for Learning Attributes based Concepts - Gilles Venturini - 1993 |
 | Similarity-Based Models of Word Cooccurrence Probabilities - Ido Dagan, Lillian Lee and Fernando C. N. Pereira - 1999 |
 | A simple algorithm for learning O(log n)-term DNF - Eyal Kushilevitz - 1996 |
 | A simple algorithm for predicting nearly as well as the best pruning labeled with the best prediction values of a decision tree - Eiji Takimoto, Ken'ichi Hirai and Akira Maruoka - 1997 |
 | A Simple Approach to Ordinal Classification - Eibe Frank and Mark Hall - 2001 |
 | A Simple Decomposition Method for Support Vector Machines - Chih-Wei Hsu and Chih-Jen Lin - 2002 |
 | Simple DFA are polynomially probably exactly learnable from simple examples - Rajesh Parekh and Vasant Honavar - 1999 |
 | Simple Flat Languages: A Learnable Class in the Limit from Positive Data - T. Okadome - 1999 |
 | A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems - David J. Hand and Robert J. Till - 2001 |
 | A Simple Greedy Algorithm for Finding Functional Relations: Efficient Implementation and Average Case Analysis - Tatsuya Akutsu, Satoru Miyano and Satoru Kuhara - 2000 |
 | Simple learning algorithms for decision trees and multivariate polynomials - Nader H. Bshouty and Yishay Mansour - 1995 |
 | Simple learning algorithms using divide and conquer - Nader H. Bshouty - 1995 |
 | A Simple Lemma on Greedy Approximation in Hilbert Space and Convergence Rates for Projection Pursuit Regression and Neural Network Training - L. K. Jones - 1992 |
 | A Simple Method for Generating Additive Clustering Models with Limited Complexity - Michael D. Lee - 2002 |
 | Simple PAC learning of simple decision lists - Jorge Castro and José L. Balcázar - 1995 |
 | A Simpler Analysis of the Multi-way Branching Decision Tree Boosting Algorithm - Kohei Hatano - 2001 |
 | Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning - Ronald J. Williams - 1992 |
 | Simple Translation-Invariant Concepts Are Hard to Learn - M. Jerrum - 1994 |
 | Simplified support vector decision rules - Chris J. C. Burges - 1996 |
 | Simplifying Decision Trees - J. R. Quinlan - 1987 |
 | A Simulated Annealing-Based Learning Algorithm for Boolean DNF - Andreas Alexander Albrecht and Kathleen Steinhöfel - 1999 |
 | Simulating Access to hidden information while learning - P. Auer and P. Long - 1994 |
 | Simulating Teams with Many Conjectures - Bala Kalyanasundaram and Mahendran Velauthapillai - 1995 |
 | Simulating the Child's Acquisition of the Lexicon and Syntax - Experiences with Babel - Rick Kazman - 1994 |
 | Simulation results for a new two-armed bandit heuristic - Ronald L. Rivest and Yiqun Yin - 1994 |
 | Simultaneous learning of concepts and simultaneous estimation of probabilities - K. Buescher and P. R. Kumar - 1991 |
 | SISP/1, an interactive system able to synthesize functions from examples - J. P. Jouannaud and T. P. Treuil - 1977 |
 | SLUG: A Connectionist Architecture for Inferring the Structure of Finite-State Environments - Michael C. Mozer and Jonathan Bachrach - 1991 |
 | Small sample decision tree pruning - Sholom M. Weiss and Nitin Indurkhya - 1994 |
 | Smooth Boosting and Learning with Malicious Noise - Rocco A. Servedio - 2001 |
 | Smoothed Bootstrap and Statistical Data Cloning for Classifier Evaluation - Gregory Shakhnarovich, Ran El-Yaniv and Yoram Baram - 2001 |
 | Smoothing Probabilistic Automata: An Error-Correcting Approach - Pierre Dupont and Juan-Carlos Amengual - 2000 |
 | SOAR: An architecture for General Intelligence - J. E. Laird, A. Newell and P. S. Rosenbloom - September 1987 |
 | Social Agents Playing a Periodical Policy - Ann Nowé, Johan Parent and Katja Verbeeck - 2001 |
 | Soft classification, a.k.a. risk estimation, via penalized log likelihood and smoothing spline analysis of variance - Grace Wahba, Chong Gu, Yuedong Wang and Richard Chappell - 1995 |
 | Soft Margins for AdaBoost - G. Rätsch, T. Onoda and K.-R. Müller - 2001 |
 | A solution of the syntactical induction-inference problem for regular languages - T. W. Pao and J. W. I. Carr - 1978 |
 | A solution of the syntactical induction-inference problem for regular languages - T. W. Pao - 1978 |
 | Solution to inductive inference problem P3 - J. Case and M. Fulk - 1983 |
 | Solving a huge number of similar tasks: a combination of multi-task learning and a hierarchical Bayesian approach - Tom Heskes - 1998 |
 | Solving Multiclass Learning Problems via Error-Correcting Output Codes - T. G. Dietterich and G. Bakiri - 1995 |
 | Solving POMDPs with Levin search and EIRA - Marco Wiering and Jürgen Schmidhuber - 1996 |
 | Solving the Multiple-Instance Problem: A Lazy Learning Approach - Jun Wang and Jean-Daniel Zucker - 2000 |
 | Some classes of prolog programs inferable from positive data - M. R. K. Krishna Rao - 2000 |
 | Some computational lower bounds for the computational complexity of inductive logic programmming - Jorg-Uwe Kietz - 1993 |
 | Some Criterions for Selecting the Best Data Abstractions - Makoto Haraguchi and Yoshimitsu Kudoh - 2001 |
 | Some Decidability results on Grammatical Inference and Complexity - J. Feldman - 1972 |
 | Some elements of machine learning - J. R. Quinlan - 1999 |
 | Some Greed Algorithms for Sparce Nonlinear Regression - Prasanth B. Nair, Arindam Choudhury and Andy J. Keane - 2001 |
 | Some ideas on learning with directional feedback - I. Barland - June 1992 |
 | Some improved sample complexity bounds in the probabilistic PAC learning model - Jun-ichi Takeuchi - 1993 |
 | Some Improvements on Event-Sequence Temporal Region Methods - Wei Zhang - 2000 |
 | Some Independence Results for Control Structures in Complete Numberings - Sanjay Jain and Jochen Nessel - 2001 |
 | Some Label Efficient Learning Results - David Helmbold and Sandra Panizza - 1997 |
 | Some Local Measures of Complexity of Convex Hulls and Generalization Bounds - Olivier Bousquet, Vladimir Koltchinskii and Dmitriy Panchenko - 2002 |
 | Some Lower Bounds for the Computational Complexity of Inductive Logic Programming - Jörg-Uwe Kietz - 1993 |
 | Some natural properties of strong identification in inductive inference - E. Minicozzi - 1976 |
 | Some New Directions in Computational Learning Theory - M. Frazier and L. Pitt - 1994 |
 | Some Notes on Chernoff Bounds - R. H. Sloan - 1987 |
 | Some PAC-Bayesian Theorems - David A. McAllester - 1999 |
 | Some Philosophical Problems with Formal Learning Theory - J. Amsterdam - August 1988 |
 | Some problems of learning with an oracle - E. B. Kinber - 1990 |
 | Some Problems on Inductive Inference from Positive Data - T. Shinohara - 1986 |
 | Some remarks about space-complexity of learning, and circuit complexity of recognizing - S. Boucheron and J. Sallantin - 1988 |
 | Some Results in the Theory of Effective Program Synthesis - Learning by Defective Information - G. Schäfer-Richter - 1986 |
 | Some Results on Learning - B. K. Natarajan - 1989 |
 | Some Sequence Extrapolating Programs: a Study of Representation and Modeling in Inquiring Systems - S. Persson - 1966 |
 | Some Sparse Approximation Bounds for Regression Problems - Tong Zhang - 2001 |
 | Some Special Vapnik-Chervonenkis Classes - R. S. Wenocur and R. M. Dudley - 1981 |
 | Some Statistical-Estimation Methods for Stochastic Finite-State Transducers - David Picó and Francisco Casacuberta - 2001 |
 | Some studies in machine learning using the game of checkers - A. L. Samuel - July 1959 |
 | Some theorems concerning the free energy of (un)constrained stochastic Hopfield neural networks - Jan van den Berg and Jan C. Bioch - 1995 |
 | Some Theoretical Aspects of Boosting in the Presence of Noisy Data - Wenxin Jiang - 2001 |
 | Some weak learning results - D. P. Helmbold and M. K. Warmuth - 1992 |
 | Sonar-based mapping with mobile robots using EM - Wolfram Burgard, Dieter Fox, Hauke Jans, Christian Matenar and Sebastian Thrun - 1999 |
 | A space-bounded learning algorithm for axis-parallel rectangles - Foued Ameur - 1995 |
 | Space-bounded learning and the Vapnik-Chervonenkis dimension - S. Floyd - 1989 |
 | Space-bounded learning and the Vapnik-Chervonenkis Dimension (Ph.D) - S. Floyd - December 1989 |
 | Space Efficient Learning Algorithms - D. Haussler - 1988 |
 | The Space of Jumping Emerging Patterns and Its Incremental Maintenance Algorithms - Jinyan Li and Kotagiri Ramamohanarao - 2000 |
 | SPADE: An Efficient Algorithm for Mining Frequent Sequences - Mohammed J. Zaki - 2001 |
 | Sparse Distributed Memory - P. Kanerva - 1988 |
 | Sparse Greedy Matrix Approximation for Machine Learning - Alex J. Smola and Bernhard Schölkopf - 2000 |
 | Sparse Regression Ensembles in Infinite and Finite Hypothesis Spaces - Gunnar Rätsch, Ayhan Demiriz and Kristin P. Bennett - 2002 |
 | A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes - Michael Kearns, Yishay Mansour and Andrew Y. Ng - 2002 |
 | Sparsity vs. Large Margins for Linear Classifiers - Ralf Herbrich, Thore Graepel and John Shawe-Taylor - 2000 |
 | Special Issue of Machine Learning on Information Retrieval Introduction - Jaime Carbonell, Yiming Yang and William Cohen - 2000 |
 | Special Issue on Genetic Algorithms - K. D. Jong - October 1990 |
 | Specification and simulation of statistical query algorithms for efficiency and noise tolerance - Javed A. Aslam and Scott E. Decatur - 1998 |
 | A spectral lower bound technique for the size of decision trees and two level circuits - Y. Brandman, J. Hennessy and A. Orlitsky - 1990 |
 | Speeding inference by acquiring new concepts - Henry Kautz and Bart Selman - July 1992 |
 | Speeding-up nearest neighbour memories: the template tree case memory organisation - Stephan Grolimund and Jean-Gabriel Ganascia - 1996 |
 | Speeding Up Relational Reinforcement Learning through the Use of an Incremental First Order Decision Tree Learner - Kurt Driessens, Jan Ramon and Hendrik Blockeel - 2001 |
 | Speeding up the synthesis of programs from traces - A. W. Biermann, R. I. Baum and F. E. Petry - 1975 |
 | The Speed Prior: A New Simplicity Measure Yielding Near-Optimal Computable Predictions - Jürgen Schmidhuber - 2002 |
 | Sphere packing numbers for subsets of the Boolean n-cube with bounded Vapnik-Chervonenkis dimension - D. Haussler - 1995 |
 | Spiking neurons and the induction of finite state machines - Thomas Natschläger and Wolfgang Maass - 2002 |
 | Srtuctural machine learning with Galois lattice and graphs - Michel Liquiere and Jean Sallantin - 1998 |
 | Stability Analysis of Learning Algorithms for Blind Source Separation - Shun-ichi Amari, Tian-ping Chen and Andrzej Cichocki - 1997 |
 | Stability and Looping in Connectionist Models with Assymmetric Weights - S. Porat - March 1987 |
 | Stable function approximation in dynamic programming - Geoffrey J. Gordon - 1995 |
 | Stacked Regressions - Leo Breiman - 1996 |
 | Stacking bagged and dagged models - Kai Ming Ting and Ian H. Witten - 1997 |
 | The stastical mechanics of learning a rule - T. L. H. Watkin, A. Rau and M. Biehl - 1993 |
 | State-based Classification of Finger Gestures from Electromyographic Signals - Peter Ju, Leslie Pack Kaelbling and Yoram Singer - 2000 |
 | Statistical and Neural Approaches for Estimating Parameters of a Speckle Model Based on the Nakagami Distribution - Mark P. Wachowiak, Renata Smol\'ıková, Mariofanna G. Milanova and Adel S. Elmaghraby - 2001 |
 | A statistical approach to decision tree modeling - M. I. Jordan - 1994 |
 | A Statistical Approach to Learning and Generalization in Layered Neural Networks - E. Levin, N. Tishby and S. A. Solla - 1990 |
 | A Statistical Approach to Learning and Generalization in Neural Networks - E. Levin, N. Tishby and S. Solla - 1989 |
 | A Statistical Approach to Solving the EBL Utility Problem - Russell Greiner and Igor Jurišica - 1992 |
 | Statistical Mechanics of Online Learning of Drifting Concepts: A Variational Approach - Renato Vicente, Osame Kinouchi and Nestor Caticha - 1998 |
 | Statistical Methods for Analyzing Speedup Learning Experiments - Oren Etzioni and Ruth Etzioni - 1994 |
 | Statistical Models for Text Segmentation - Doug Beeferman, Adam Berger and John D. Lafferty - 1999 |
 | Statistical Properties and Adaptive Tuning of Support Vector Machines - Yi Lin, Grace Wahba, Hao Zhang and Yoonkyung Lee - 2002 |
 | Statistical queries and faulty PAC oracles - S. E. Decatur - 1993 |
 | Statistical Sufficiency for Classes in Empirical L2 Spaces - Shahar Mendelson and Naftali Tishby - 2000 |
 | Statistical theory of generalization (abstract) - Vladimir Vapnik - 1996 |
 | Statistical Theory of Learning a Rule - G'eza Györgi and Naftali Tishby - 1990 |
 | Statistics of Flow Vectors and Its Application to the Voting Method for the Detection of Flow Fields - Atsushi Imiya and Keisuke Iwawaki - 2001 |
 | Statistification or Mystification? The Need for Statistical Thought in Visual Data Mining - Antony Unwin - 2001 |
 | A stochastic approach to genetic information processing - Akihiko Konagaya - 1993 |
 | Stochastic Complexity and Modeling - J. Rissanen - 1986 |
 | Stochastic Complexity and Sufficient Statistics - J. Rissanen - 1986 |
 | Stochastic Complexity in Learning - Jorma Rissanen - 1997 |
 | Stochastic Complexity in Statistical Inquiry - J. Rissanen - 1989 |
 | Stochastic Context-Free Grammars for tRNA modeling - Yasubumi Sakakibara, Michael Brown, Richard Hughey, I. Saira Mian, Kimmen Sjölander, Rebecca C. Underwood and David Haussler - 1994 |
 | Stochastic Finite Learning - Thomas Zeugmann - 2001 |
 | Stochastic Finite Learning of the Pattern Languages - Peter Rossmanith and Thomas Zeugmann - 2001 |
 | Stochastic Grammatical Inference of Text Database Structure - Matthew Young-Lai and Frank WM. Tompa - 2000 |
 | Stochastic Inference of Regular Tree Languages - Rafael C. Carrasco, Jose Oncina and Jorge Calera-Rubio - 2001 |
 | Stochastic inference of regular tree languages - Rafael C. Carrasco, Jose Oncina and Jorge Calera - 1998 |
 | Stochastic Relaxation Methods for Image Restoration and Expert Systems - S. Geman - 1986 |
 | Stochastic resonance with adaptive fuzzy systems - Sanya Mitaim and Bart Kosko - 1998 |
 | A stochastic search approach to grammar induction - Hugues Juillé and Jordan B. Pollack - 1998 |
 | Stochastic simple recurrent neural networks - Mostefa Golea, Masahiro Matsuoka and Yasubumi Sakakibara - 1996 |
 | Strategies for Teaching Layered Networks Classification Tasks - B. S. Wittner and J. S. Denker - 1988 |
 | Strategies in Combined Learning via Logic Programs - E. Lamma, F. Riguzzi and L. M. Pereira - 2000 |
 | Strategy Under the Unknown Stochastic Environment: The Nonparametric Lob-Pass Problem - K. Hiraoka and S. Amari - 1998 |
 | Stratified Inductive Hypothesis Generation - Zs. Szabó - 1986 |
 | The strength of noninclusions for teams of finite learners - M. Kummer - 1994 |
 | The Strength of Weak Learnability - Robert E. Schapire - 1990 |
 | Strong Entropy Concentration, Game Theory and Algorithmic Randomness - Peter Grünwald - 2001 |
 | Strong minimax lower bounds for learning - András Antos and Gábor Lugosi - 1998 |
 | Strong monotonic and set-driven inductive inference - Sanjay Jain - 1997 |
 | Strong Separation of Learning Classes - J. Case, K. J. Chen and S. Jain - 1992 |
 | Structural measures for games and process control in the branch learning model - Matthias Ott and Frank Stephan - 2000 |
 | Structural Modelling with Sparse Kernels - S. R. Gunn and J. S. Kandola - 2002 |
 | Structural Results about Exact Learning with Unspecified Attribute Values - Andreas Birkendorf, Norbert Klasner, Christian Kuhlmann and Hans Ulrich Simon - 2000 |
 | Structural Results About On-line Learning Models With and Without Queries - Peter Auer and Philip M. Long - 1999 |
 | Structural risk minimization over data-dependent hierarchies - J. Shawe-Taylor and P. L. Bartlett - 1998 |
 | Structured Prioritized Sweeping - Richard Dearden - 2001 |
 | Structured Weight-Based Prediction Algorithms - Akira Maruoka and Eiji Takimoto - 1998 |
 | Structure in the Space of Value Functions - David Foster and Peter Dayan - 2002 |
 | The Structure of Intrinsic Complexity of Learning - Sanjay Jain and Arun Sharma - 1997 |
 | The Structure of Intrinsic Complexity of Learning - Sanjay Jain and Arun Sharma - 1995 |
 | The Structure of Scientific Discovery: From a Philosophical Point of View - Keiichi Noé - 2001 |
 | Structuring Neural Networks and PAC Learning - E. Pippig - 1995 |
 | Studies on Inductive Inference from Positive Data - T. Shinohara - 1986 |
 | A Study of Explanation-Based Methods for Inductive Learning - Nicholas S. Flann and Thomas G. Dietterich - 1989 |
 | A Study of Grammatical Inference - J. J. Horning - 1969 |
 | A Study of Inductive Inference machines - M. Fulk - 1985 |
 | A Study of Reinforcement Learning in the Continuous Case by the Means of Viscosity Solutions - Rémi Munos - 2000 |
 | A Study of Scaling and Generalization in Neural Networks - S. Ahmad - September 1988 |
 | A Study on the Performance of Large Bayes Classifier - Dimitris Meretakis, Hongjun Lu and Beat Wüthrich - 2000 |
 | The subset principle is an intensional principle - K. Wexler - 1993 |
 | Successes, Failures, and New Directions in Natural Language Learning - Claire Cardie - 2001 |
 | Suggestions for Genetic A.I. - G. L. Drescher - February 1980 |
 | Summary of the panel discussion - D. Angluin, L. Birnbaum, J. Feldman, R. Rivest and L. Valiant - 1988 |
 | Supervised and unsupervised discretization of continuous features - James Dougherty, Ron Kohavi and Mehran Sahami - 1995 |
 | Supervised learning and systems with excess degrees of freedom - M. I. Jordan - May 1988 |
 | Supervised Learning of Probability Distributions by Neural Networks - E. Baum and F. Wilczek - 1988 |
 | Supervised learning using labeled and unlabeled examples - Geoffrey Towell - 1997 |
 | Supervised Versus Unsupervised Binary-Learning by Feedforward Neural Networks - Nathalie Japkowicz - 2001 |
 | Supporting Start-to-Finish Development of Knowledge Bases - Ray Bareiss, Bruce W. Porter and Kenneth S. Murray - 1989 |
 | Support Vector Machine Active Learning with Applications to Text Classification - Simon Tong and Daphne Koller - 2000 |
 | Support Vector Machines for Classification in Nonstandard Situations - Yi Lin, Yoonkyung Lee and Grace Wahba - 2002 |
 | Support-vector networks - Corinna Cortes and Vladimir Vapnik - 1995 |
 | Support Vectors for Reinforcement Learning - Thomas G. Dietterich and Xin Wang - 2001 |
 | A supra-classifier architecture for scalable knowledge reuse - Kurt D. Bollacker and Joydeep Ghosh - 1998 |
 | A survey of computational learning theory - P. Laird - 1990 |
 | A survey of Inductive Inference: Theory and Methods - D. Angluin and C. H. Smith - September 1983 |
 | A Survey of Inductive Inference with an Emphasis on Learning via Queries - William Gasarch and Carl H. Smith - 1997 |
 | A survey of results in grammatical inference - A. W. Biermann and J. A. Feldman - 1972 |
 | A survey of the synthesis of LISP programs from examples - D. R. Smith - 1982 |
 | Symbiosis in multimodal concept learning - Jukka Hekanaho - 1995 |
 | Symbolic and Neural Learning Algorithms: An Experimental Comparison - Jude W. Shavlik, Raymond J. Mooney and Geoffrey G. Towell - 1991 |
 | Symbolic Discriminant Analysis for Mining Gene Expression Patterns - Jason H. Moore, Joel S. Parker and Lance W. Hahn - 2001 |
 | Symmetry in Markov Decision Processes and its Implications for Single Agent and Multiagent Learning - Martin Zinkevich and Tucker Balch - 2001 |
 | Synergy of clustering multiple backpropagation networks - N. Lincoln and J. Skrzypek - 1989 |
 | The syntactic inference problem for D0L sequences - P. G. Doucet - 1974 |
 | Syntactic Methods in Pattern Recognition - K. S. Fu - 1974 |
 | Syntactic Pattern Recognition, An Introduction - R. C. Gonzalez and M. G. Thomason - 1978 |
 | Syntactic Pattern Recognition, Applications - K. S. Fu - 1977 |
 | Synthesis Algorithm for Recursive Processes by mu-calculus - Shigemoto Kimura, Atsushi Togashi and Norio Shiratori - 1994 |
 | Synthesising Inductive Expertise - Daniel N. Osherson, Michael Stob and Scott Weinstein - 1988 |
 | The synthesis of language learners - Ganesh R. Baliga, John Case and Sanjay Jain - 1999 |
 | Synthesis of LISP programs from examples - S. Hardy - 1975 |
 | Synthesis of real time acceptors - Amr F. Fahmy and A. W. Biermann - 1993 |
 | Synthesis of Rewrite Programs by Higher-Order and Semantic Unification - M. Hagiya - 1991 |
 | Synthesis of UNIX Programs Using Derivational Analogy - Sanjay Bhansali and Mehdi T. Harandi - 1993 |
 | Synthesizing Context Free Grammars from Sample Strings Based on Inductive CYK Algorithm - Katsuhiko Nakamura and Takashi Ishiwata - 2000 |
 | Synthesizing enumeration techniques for language learning - Ganesh R. Baliga, John Case and Sanjay Jain - 1996 |
 | Synthesizing Learners Tolerating Computable Noisy Data - John Case and Sanjay Jain - 2001 |
 | Synthesizing Learners Tolerating Computable Noisy Data - John Case and Sanjay Jain - 1998 |
 | Synthesizing noise-tolerant language learners - John Case, Sanjay Jain and Arun Sharma - 1997 |
 | Synthesizing noise-tolerant language learners - John Case, Sanjay Jain and Arun Sharma - 2001 |
 | Synthetic Neural Modelling: Comparisons of Population and Connectionist Approaches - Jr G. N. Reeke, O. Sporns and G. M. Edelman - 1989 |
 | System Identification Via State Characterization - E. M. Gold - 1972 |
 | Systems that Learn: An Introduction to Learning Theory for Cognitive and Computer Scientists - D. N. Osherson, M. Stob and S. Weinstein - 1986 |
 | Systems that Learn: An Introduction to Learning Theory, second edition - Sanjay Jain, Daniel Osherson, James S. Royer and Arun Sharma - 1999 |