| 1996 | |  | Review of Inductive Logic Programming: Techniques and Applications by Nada Lavrac, Saso Dzeroski - Michael Pazzani |
| | | Real-World Robotics: Learning to Plan for Robust Execution - Scott W. Bennett and Gerald F. DeJong |
| | | Background knowledge in GA-based concept learning - Jukka Hekanaho |
| | | Nonparametric statistical methods for experimental evaluations of speedup learning - Geoffrey J. Gordon and Alberto Maria Segre |
| | | Simplified support vector decision rules - Chris J. C. Burges |
| | | Learning Concepts from Sensor Data of a Mobile Robot - Volker Klingspor, Katharina J. Morik and Anke D. Rieger |
| | | A simple algorithm for learning O log n -term DNF - Eyal Kushilevitz |
| | | Elementary formal systems, intrinsic complexity, and procrastination - Sanjay Jain and Arun Sharma |
| | | Experiments with a new Boosting algorithm - Yoav Freund and Robert E. Schapire |
| | | Graph learning with a nearest neighbor approach - Sven Koenig and Yury Smirnov |
| | | Learning an optimal decision strategy in an influence diagram with latent variables - V. G. Vovk |
| | | Efficient Learning of One-Variable Pattern Languages from Positive Examples - T. Erlebach, P. Rossmanith, H. Stadtherr, A. Steger and T. Zeugmann |
| | | Learning of depth two neural networks with constant fan-in at the hidden nodes - Peter Auer, Stephen Kwek, Wolfgang Maass and Manfred K. Warmuth |
| | | On learning width two branching programs - Nader H. Bshouty, Christino Tamon and David K. Wilson |
| | | Applying winnow to context-sensitive spelli ng correction - Andrew R. Golding and Dan Roth |
| | | The dual DFA learning problem: hardness results for programming by demonstration and learning first-order representations - William W. Cohen |
| | | On Learning Visual Concepts and DNF Formulae - Eyal Kushilevitz and Dan Roth |
| | | Learning to Select Useful Landmarks - Russell Greiner and Ramana Isukapalli |
| | | Book review: inductive logic programming: techniques and applications - Michael Pazzani |
| | | Efficient learning of selective Bayesian network classifiers - Moninder Singh and Gregory M. Provan |
| | | Experimental knowledge acquisition for planning - Kang Soo Tae and Diane J. Cook |
| | | VC dimension of an integrate-and-fire neuron model - Anthony M. Zador and Barak A. Pearlmutter |
| | | CLASSIC Learning - Michael Frazier and Leonard Pitt |
| | | Feature-Based Methods for Large Scale Dynamic Programming - John N. Tsitsiklis and Benjamin van Roy |
| | | Approximating value trees in structured dynamic programming - Craig Boutilier and Richard Dearden |
| | | Scaling Up Inductive Learning with Massive Parallelism - Foster John Provost and John M. Aronis |
| | | On the Intrinsic Complexity of Learning - R. Freivalds, E. Kinber and C. Smith |
| | | Analysis of greedy expert hiring and an application to memory-based learning - Igal Galperin |
| | | Towards robust model selection using estimation and approximation error bounds - Joel Ratsaby, Ronny Meir and Vitaly Maiorov |
| | | Algorithms and applications for multitask learning - Rich Caruana |
| | | Technical note: incremental multi-step Q-learning - Jing Peng and Ronald J. Williams |
| | | Learning Controllers for Industrial Robots - C. Baroglio, A. Giordana, M. Kaiser, M. Nuttin and R. Piola |
| | | Learning branches and learning to win closed games - Martin Kummer and Matthias Ott |
| | | Angluin’s theorem for indexed families of r.e. sets and applications - Dick de Jongh and Makoto Kanazawa |
| | | Learning binary perceptrons perfectly efficiently - Shao C. Fang and Santosh S. Venkatesh |
| | | Exploration Bonuses and Dual Control - Peter Dayan and Terrence J. Sejnowski |
| | | Scaling up average reward reinforcement learning by approximating the domain models and the value function - Prasad Tadepalli and DoKyeong Ok |
| | | Searching for structure in multiple streams of data - Tim Oates and Paul R. Cohen |
| | | On-line adaptation of a signal predistorter through dual reinforcement learning - Patrick Goetz, Shailesh Kumar and Risto Miikkulainen |
| | | Efficient Reinforcement Learning through Symbiotic Evolution - David E. Moriarty and Risto Miikkulainen |
| | | Efficient Incremental Induction of Decision Trees - Dimitrios Kalles and Tim Morris |
| | | A theoretical and empirical study of a noise-tolerant algorithm to learn geometric patterns - Sally A. Goldman and Stephen D. Scott |
| | | Non mean square error criteria for the training of learning machines - Marco Saerens |
| | | Teaching a smarter learner - Sally A. Goldman and H. David Mathias |
| | | A Bayesian/information theoretic model of bias learning - Jonathan Baxter |
| | | representing and learning quality-improving search control knowledge - M. Alicia Pérez |
| | | Introduction - Judy A. Franklin, Tom M. Mitchell and Sebastian Thrun |
| | | Recognition and exploitation of contextual clues via incremental meta-learning - Gerhard Widmer |
| | | The Power of Amnesia: Learning Probabilistic Automata with Variable Memory Length - Dana Ron, Yoram Singer and Naftali Tishby |
| | | Monotonic and dual monotonic language learning - S. Lange, T. Zeugmann and S. Kapur |
| | | The importance of convexity in learning with squared loss - Wee Sun Lee, Peter L. Bartlett and Robert C. Williamson |
| | | Probabilistic instance-based learning - Henry Tirri, Petri Kontkanen and Petri Myllymäki |
| | | Improving the efficiency of knowledge base refinement - Leonardo Carbonara and Derek Sleeman |
| | | Active Learning for Vision-Based Robot Grasping - Marcos Salganicoff, Lyle H. Ungar and Ruzena Bajcsy |
| | | A probabilistic approach to feature selection - a filter solution - Huan Liu and Rudy Setiono |
| | | Unsupervised learning using MML - Jonathan J. Oliver, Rohan A. Baxter and Chris S. Wallace |
| | | Probabilistic and team PFIN-type learning: general properties - Andris Ambainis |
| | | Learning word association norms using tree cut pair models - Naoki Abe and Hang Li |
| | | Synthesizing enumeration techniques for language learning - Ganesh R. Baliga, John Case and Sanjay Jain |
| | | On-line Prediction and Conversion Strategies - Nicolo Cesa-Bianchi, Yoav Freund, David P. Helmbold and Manfred K. Warmuth |
| | | Worst-Case Loss Bounds for Sigmoided Linear Neurons - D. P. Helmbold, J. Kivinen and M. K. Warmuth |
| | | Learning goal oriented Bayesian networks for telecommunications risk management - Kazuo J. Ezawa, Moninder Singh and Steven W. Norton |
| | | Incremental Multi-Step Q-Learning - Jing Peng and Ronald J. Williams |
| | | A Reply to Pazzani’s Book Review of Inductive Logic Programming: Techniques and Applications - Nada Lavrac and Saso Dzeroski |
| | | Analysis of a simple learning algorithm: learning foraging thresholds for lizards - Leslie Ann Goldberg, William E. Hart and David Bruce Wilson |
| | | Learning evaluation functions for large acyclic domains - Justin A. Boyan and Andrew W. Moore |
| | | Relational instance-based learning - Werner Emde and Dietrich Wettschereck |
| | | A convergent reinforcement learning algorithm in the continuous case: the finite-element reinforcement learning - Rémi Munos |
| | | Learning changing concepts by exploiting the structure of change - Peter L. Bartlett, Shai Ben-David and Sanjeev R. Kulkarni |
| | | Identifying the information contained in a flawed theory - Sean P. Engelson and Moshe Koppel |
| | | Negative robust learning results for Horn clause programs - Pascal Jappy, Richard Nock and Olivier Gascuel |
| | | Delaying the choice of bias: a disjunctive version space approach - Michele Sebag |
| | | Second tier for decision trees - Miroslav Kubat |
| | | Solving POMDPs with Levin search and EIRA - Marco Wiering and Jürgen Scmidhuber |
| | | Noise-Tolerant Distribution-Free Learning of General Geometric Concepts - Bshouty, Goldman, Mathias, Suri and Tamaki |
| | | Learning Bayesian belief networks based on the minimum description length principle: an efficient algorithm using the B \& B technique - Joe Suzuki |
| | | Non-linear decision trees - NDT - Andreas Ittner and Michael Schlosser |
| | | Co-Learning of Recursive Languages from Positive Data - R. Freivalds and T. Zeugmann |
| | | Linear least-squares algorithms for temporal difference learning - Steven J. Bradtke and Andrew G. Barto |
| | | PAC learning axis-aligned rectangles with respect to product distributions from multiple-instance examples - Philip M. Long and Lei Tan |
| | | Learning despite concept variation by finding structure in attribute-based data - Eduardo Pérez and Larry A. Rendell |
| | | Discovering structure in multiple learning tasks: the TC algorithm - Sebastian Thrun and Joseph O’Sullivan |
| | | BEXA: A Covering Algorithm for Learning Propositional Concept Descriptions - Hendrik Theron and Ian Cloete |
| | | The loss from imperfect value functions in expectation-based and minimax-based tasks - Matthias Heger |
| | | Performance Improvement of Robot Continuous-Path Operation through Iterative Learning Using Neural Networks - Peter C. Y. Chen, James K. Mills and Kenneth C. Smith |
| | | Exploiting the omission of irrelevant data - Russell Greiner, Adam J. Grove and Alexander Kogan |
| | | Statistical theory of generalization abstract - Vladimir Vapnik |
| | | Asking questions to minimize errors - Nader H. Bshouty, Sally A. Goldman, Thomas R. Hancock and Sleiman Matar |
| | | Analogy access by mapping spreading and abstraction in large, multifunctional knowledge bases - Davide Roverso |
| | | PAC Learning of One-Dimensional Patterns - Paul W. Goldberg, Sally A. Goldman and Stephen D. Scott |
| | | Reinforcement Learning with Replacing Eligibility Traces - Satinder P Singh and Richard S. Sutton |
| | | Passive distance learning for robot navigation - Sven Koenig and Reid G. Simmons |
| | | General bounds on the number of examples needed for learning probabilistic concepts - Hans Ulrich Simon |
| | | Learning by Erasing - S. Lange, R. Wiehagen and T. Zeugmann |
| | | Predicting a binary sequence almost as well as the optimal biased coin - Yoav Freund |
| | | Representation changes for efficient learning in structural domains - Jean-Daniel Zucker and Jean-Gabriel Ganascia |
| | | Data mining and machine learning abstract - Heikki Mannila |
| | | A framework for structural risk minimization - John Shawe-Taylor, Peter L. Bartlett, Robert C. Williamson and Martin Anthony |
| | | Editorial on new Machine Learning website - Thomas G. Dietterich |
| | | Actual return reinforcement learning versus temporal differences: some theoretical and experimental results - Mark D. Pendrith and Malcolm R. K. Ryan |
| | | Reinforcement learning in factories: the auton project abstract - Andrew W. Moore |
| | | Theory-guided empirical speedup learning of goal decomposition rules - Chandra Reddy, Prasad Tadepalli and Silvana Roncagliolo |
| | | Learning curve bounds for a Markov decision process with undiscounted rewards - Lawrence K. Saul and Satinder P. Singh |
| | | Incremental Learning from Positive Data - S. Lange and T. Zeugmann |
| | | A Decision-Tree Model of Balance Scale Development - William C. Schmidt and Charles X. Ling |
| | | Strong minimax lower bounds for learning - András Antos and Gábor Lugosi |
| | | A generalized reinforcement-learning model:convergence and applications - Michael L. Littman and Csaba Szepesvári |
| | | Toward optimal feature selection - Daphne Koller and Mehran Sahami |
| | | Learning active classifiers - Russell Greiner, Adam J. Grove and Dan Roth |
| | | Game theory, on-line prediction and boosting - Yoav Freund and Robert E. Schapire |
| | | Speeding-up nearest neighbour memories: the template tree case memory organisation - Stephan Grolimund and Jean-Gabriel Ganascia |
| | | On the learnability of the uncomputable - Richard H. Lathrop |
| | | Applying the multiple cause mixture model to text categorization - Mehran Sahami, Marti Hearst and Eric Saund |
| | | Theoretical analysis of the nearest neighbor classifier in noisy domains - Seishi Okamoto and Nobuhiro Yugami |
| | | K nearest neighbor classification on feature projections - s Aynur Akku\ and H. Altay Güvenir |
| | | Learning sparse multivariate polynomials over a field with queries and counterexamples - Robert E. Schapire and Linda M. Sellie |
| | | Challenges in machine learning for text classification - David D. Lewis |
| | | The characterisation of predictive accuracy and decision combination - Kai Ming Ting |
| | | Constructive induction using fragmentary knowledge - Steve Donoho and Larry Rendell |
| | | Robot Programming by Demonstration RPD : Supporting the Induction by Human Interaction - H. Friedrich, S. Münch, R. Dillman, S. Bocionek and M. Sassin |
| | | On the Worst-Case Analysis of Temporal-Difference Learning Algorithms - Robert E. Schapire and Manfred K. Warmuth |
| | | Toward a model of mind as a laissez-faire economy of idiots - Eric B. Baum |
| | | Error Reduction through Learning Multiple Descriptions - Kamal M. Ali and Michael J. Pazzani |
| | | Learning radial basis function networks on-line - E. Blanzieri and P. Katenkamp |
| | | Theory-guided induction of logic programs by inference of regular languages - Henrik Boström |
| | | On the complexity of learning from drifting distributions - Rakesh D. Barve and Philip M. Long |
| | | Technical Note: Some Properties of Splitting Criteria - Leo Breiman |
| | | Guest Editor’s Introduction by Thomas Hancock - Thomas Hancock |
| | | Bias plus variance decomposition for zero-one loss functions - Ron Kohavi and David H. Wolpert |
| | | PAC-like upper bounds for the sample complexity of leave-one-out cross-validation - Sean B. Holden |
| | | Lower bound on learning decision lists and trees - Thomas Hancock, Tao Jiang, Ming Li and John Tromp |
| | | The Effect of Representation and Knowledge on Goal-Directed Exploration with Reinforcement-Learning Algorithms - Sven Koenig and Reid G. Simmons |
| | | Sensitive discount optimality: unifying discounted and average reward reinforcement learning - Sridhar Mahadevan |
| | | Classification by feature partitioning - H. Altay Güvenir and Izzet Sirin |
| | | Using the Minimum Description Length Principle to Infer Reduced Ordered Decision Graphs - Arlindo L. Oliveira and Alberto Sangiovanni-Vincentelli |
| | | Set-driven and rearrangement-independent learning of recursive languages - S. Lange and T. Zeugmann |
| | | On-line portfolio selection using multiplicative updates - David P. Helmbold, Robert E. Schapire, Yoram Singer and Manfred K. Warmuth |
| | | On Bayes methods for on-line Boolean prediction - Nicolò Cesa-Bianchi, David P. Helmbold and Sandra Panizza |
| | | Learning relational concepts with decision trees - Peter Geibel and Fritz Wysotzki |
| | | Beyond independence: conditions for the optimality of the simple Bayesian classifier - Pedro Domingos and Michael Pazzani |
| | | Applying the weak learning framework to understand and improve C4.5 - Tom Dietterich, Michael Kearns and Yishay Mansour |
| | | Representation of finite state automata in recurrent radial basis function networks - Paolo Frasconi, Marco Gori, Marco Maggini and Giovanni Soda |
| | | Discretizing continuous attributes while learning Bayesian neworks - Nir Friedman and Moises Goldszmidt |
| | | PAC learning intersections of halfspaces with membership queries - Stephen Kwek and Leonard Pitt |
| | | Causal discovery via MML - Chris Wallace, Kevin B. Korb and Honghua Dai |
| | | On restricted-focus-of-attention learnability of Boolean functions - Andreas Birkendorf, Eli Dichterman, Jeffrey Jackson, Norbert Klasner and Hans Ulrich Simon |
| | | A data dependent skeleton estimate for learning - Gábor Lugosi and Márta Pintér |
| | | Introduction - Leslie Pack Kaelbling |
| | | Exact classification with two-layer neural nets - Gavin J. Gibson |
| | | Residual Q-learning applied to visual attention - Cesar Bandera, Francisco J. Vico, Jose M. Bravo, Mance E. Harmon and Leemon C. Baird III |
| | | A competitive approach to game learning - Christopher D. Rosin and Richard K. Belew |
| | | Average Reward Reinforcement Learning: Foundations, Algorithms, and Empirical Results - Sridhar Mahadevan |
| | | Stacked Regressions - Leo Breiman |
| | | Creating Advice-Taking Reinforcement Learners - Richard Maclin and Jude W. Shavlik |
| | | On the Limits of Proper Learnability of Subclasses of DNF Formulas - Pillaipakkamnatt Krishnan and Raghavan Vijay |
| | | Learning in the Presence of Concept Drift and Hidden Contexts - Gerhard Widmer and Miroslav Kubat |
| | | Exponentially many local minima for single neurons - P. Auer, M. Herbster and M. K. Warmuth |
| | | Trees and learning - Wolfgang Merkle and Frank Stephan |
| | | An advanced evolution should not repeat its past errors - Caroline Ravis’e and Michèle Sebag |
| | | On-line portfolio selection - Erik Ordentlich and Thomas Cover |
| | | A randomized approximation of the MDL for stochastic models with hidden variables - Kenji Yamanishi |
| | | Worst-case Loss Bounds for Single Neurons - D. P. Helmbold, J. Kivinen and M. K. Warmuth |
| | | Learning conjunctions of two unate DNF formulas: computational and informational results - Aaron Feigelson and Lisa Hellerstein |
| | | On the structure of the Degrees of Inferability - Martin Kummer and Frank Stephan |
| | | Purposive Behavior Acquisition for a Real Robot by Vision-Based Reinforcement Learning - Minoru Asada, Shoichi Noda, Sukoya Tawaratsumida and Koh Hosoda |
| | | Attribute-efficient learning in query and mistake-bound models - Nader H. Bshouty and Lisa Hellerstein |
| April | | Probably Approximately Optimal Satisficing Strategies - Russell Greiner and Pekka Orponen |
| July | | PALO: A probabilistic hill-climbing algorithm - Russell Greiner |
