1993 | | | **Cryptographic hardness of distribution-specific learning** - *M. Kharitonov* |

| | | **A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features** - *Scott Cost and Steven Salzberg* |

| | | **Improving Example-Guided Unfolding** - *Henrik Boström* |

| | | **An Application of Machine Learning in the Domain of Loan Analysis** - *José Ferreira, Joaquim Correia, Thomas Jamet and Ernesto Costa* |

| | | **Genetic Algorithms for Protein Tertiary Structure Prediction** - *Steffen Schulze-Kreme* |

| | | **Algebraic Structure of some Learning Systems** - *Jean-Gabriel Ganascia* |

| | | **Learning and robust learning of product distributions** - *K. Höffgen* |

| | | **Universal Grammar and Learnability Theory: The Case of Binding Domains and the `Subset Principle'** - *S. Kapur, B. Lust, W. Harbert and G. Motohardjono* |

| | | **Extraction of Knowledge from Data Using Constrained Neural Networks** - *Raqui Kane, Irina Tchoumatchenko and Maurice Milgram* |

| | | **Learning Causal Patterns: Making a Transition from Data-Driven to Theory-Driven Learning** - *Michael Pazzani* |

| | | **On the power of sigmoid neural networks** - *J. Kilian and H. Siegelmann* |

| | | **Learning Strategies Using Decision Lists** - *Satoshi Kobayashi* |

| | | **On the Proper Definition of Minimality in Specialization and Theory Revision** - *Stefan Wrobel* |

| | | **Bayes and Pseudo-Bayes Estimates of Conditional Probabilities and Their Reliability** - *James Cussens* |

| | | **Identifying and Using Patterns in Sequential Data** - *Philip Laird* |

| | | **Multistrategy Learning and Theory Revision** - *Lorenza Saitta, Marco Botta and Filippo Neri* |

| | | **Funtional Inductive Logic Programming with Queries to the User** - *Francesco Bergadano and Daniele Gunetti* |

| | | **Selecting a Classification Method by Cross-Validation** - *Cullen Schaffer* |

| | | **More About Learning Elementary Formal Systems** - *Setsuo Arikawa, Takeshi Shinohara, Satoru Miyano and Ayumi Shinohara* |

| | | **Competition-Based Induction of Decision Models from Examples** - *David Perry Greene and Stephen F. Smith* |

| | | **C4.5: Programs for machine learning** - *J. R. Quinlan* |

| | | **Refinement of Rule Sets with JoJo** - *Dieter Fensel and Markus Wiese* |

| | | **Predicate Invention in Inductive Data Engineering** - *Peter A. Flach* |

| | | **Learning read-once formulas with queries** - *Dana Angluin, Lisa Hellerstein and Marek Karpinski* |

| | | **Generalized Unification as Background Knowledge in Learning Logic Programs** - *Akihiro Yamamoto* |

| | | **Inductive Resolution** - *Taisuke Sato and Sumitaka Akiba* |

| | | **Discovery learning in intelligent tutoring systems** - *Setsuo Otsuki* |

| | | **Decision Tree Pruning as a Search in the State Spac** - *Floriana Esposito, Donato Malerba and Giovanni Semeraro* |

| | | **On-line learning with linear loss constraints** - *N. Littlestone and P. Long* |

| | | **Polynomial-time MAT learning of multilinear logic programs** - *Kimihito Ito and Akihiro Yamamoto* |

| | | **Learning Decision Trees using the Fourier Spectrum** - *E. Kushilevitz and Y. Mansour* |

| | | **On the complexity of learning strings and sequences** - *Tao Jiang and Ming Li* |

| | | **On the query complexity of learning** - *S. Kannan* |

| | | **A Reply to Cohen's Book Review of Creating a Memory of Causal Relationships** - *Michael Pazzani* |

| | | **Pac-Learning a Restricted Class of Recursive Logic Programs** - *William Cohen* |

| | | **On the sample complexity of various learning strategies in the probabilistic PAC learning paradigms** - *N. Abe* |

| | | **Erratum** - *No Author* |

| | | **On the non-existence of maximal inference degrees for language identification** - *S. Jain and A. Sharma* |

| | | **Learning to Control Dynamic Systems with Automatic Quantization** - *Charles X. Ling and Ralph Buchal* |

| | | **Parameterized learning complexity** - *R. Downey, P. Evans and M. Fellows* |

| | | **Finiteness results for sigmoid** - *A. Macintyre and E. D. Sontag* |

| | | **Efficient noise-tolerant learning from statistical queries** - *M. Kearns* |

| | | **Directed drift: A new linear threshold algorithm for learning binary weights on-line** - *Santosh S. Venkatesh* |

| | | **On probably correct classification of concepts** - *S. Kulkarni and O. Zeitouni* |

| | | **Integrating Feature Extraction and Memory Search** - *Christopher Owens* |

| | | **Machine Learning: From Theory to Applications; Cooperative Research at Siemens and MIT** - *S. J. Hanson and W. Remmele and Ronald L. Rivest* |

| | | **A model of sequence extrapolation** - *P. Laird, S. R and P. Dunning* |

| | | **Planning with abstraction based on partial predicate mappings** - *Yoshiaki Okubo and Makoto Haraguchi* |

| | | **Discovering Patterns in EEG-Signals: Comparative Study of a Few Methods** - *Miroslav Kubat, Doris Flotzinger and Gert Pfurtscheller* |

| | | **Occam's razor for functions** - *B. K. Natarajan* |

| | | **Language learning in dependence on the space of hypotheses** - *S. Lange and T. Zeugmann* |

| | | **Learning unions of two rectangles in the plane with equivalence queries** - *Z. Chen* |

| | | **On polynomial-time probably almost discriminative learnability** - *K. Yamanishi* |

| | | **Extracting Refined Rules from Knowledge-Based Neural Networks** - *Geoffrey G. Towell and Jude W. Shavlik* |

| | | **On bounded queries and approximation** - *Richard Chang and William I. Gasarch* |

| | | **Language Learning with a Bounded Number of Mind Changes** - *S. Lange and T. Zeugmann* |

| | | **Rates of convergence for minimum contrast estimators** - *L. Birge and P. Massart* |

| | | **Learning with Minimal Number of Queries** - *S. Matar* |

| | | **Feature Selection Using Rough Sets Theory** - *Maciej Modrzejewski* |

| | | **On Choosing between Experimenting and Thinking when Learning** - *Ronald L. Rivest and Robert H. Sloan* |

| | | **Computational Limits on Team Identification of Languages** - *S. Jain and A. Sharma* |

| | | **Proceedings of the Sixth Annual ACM Conference on Computational Learning Theory** - *Lenny Pitt* |

| | | **On learning in the limit and non-uniform (***epsilon* , *delta* )- learning - *S. Ben-David and M. Jacovi* |

| | | **Cryptographic Limitations on Learning One-Clause Logic Programs** - *William Cohen* |

| | | **Efficient inductive inference of primitive prologs from positive data** - *Hiroki Ishizaka, Hiroki Arimura and Takeshi Shinohara* |

| | | **Uniform Characterizations of Various Kinds of Language Learning** - *Shyam Kapur* |

| | | **Reformulation of Explanation by Linear Logic - Toward Logic for Explanation -** - *Jun Arima and Hajime Sawamura* |

| | | **A stochastic approach to genetic information processing** - *Akihiko Konagaya* |

| | | **A polynomial time algorithm for finding finite unions of tree pattern languages** - *H. Arimura, T. Shinohara and S. Otsuki* |

| | | **Noise-Tolerant Occam Algorithms and Their Applications to Learning Decision Trees** - *Yasubumi Sakakibara* |

| | | **On the duality between mechanistic learners and what it is they learn** - *Rīsiņš Freivalds and Carl H. Smith* |

| | | **From inductive inference to algorithmic learning theory** - *Rolf Wiehagen* |

| | | **Generalization under Implication by using Or-Introduction** - *Peter Idestam-Almquist* |

| | | **Prioritized Sweeping: Reinforcement Learning With Less Data and Less Time** - *Andrew W. Moore and Christopher G. Atkeson* |

| | | **Linear time deterministic learning of k-term DNF** - *U. Berggren* |

| | | **Discovery by Minimal Length Encoding: A Case Study in Molecular Evolution** - *Aleksandar Milosavljević and Jerzy Jurka* |

| | | **Two Methods for Improving Inductive Logic Programming Systems** - *Irene Stahl, Birgit Tausend and Rüdiger Wirth* |

| | | **Towards efficient inductive synthesis of expressions from input/output examples** - *Jānis Bārzdiņs, Guntis Bārzdiņs, Kalvis Aps\=ıtis and Uğis Sarkans* |

| | | **General bounds on statistical query learning and PAC learning with noise via hypothesis boosting** - *Javed A. Aslam and Scott E. Decatur* |

| | | **IDDD: An Inductive, Domain Dependent Decision Algorithm.** - *L. Gaga, V. Moustakis, G. Charissis and S. Orphanoudakis* |

| | | **Notes on the PAC learning of geometric concepts with additional information** - *Ken-ichiro Kakihara and Hiroshi Imai* |

| | | **Creating a Memory of Casual Relationships** - *William W. Cohen* |

| | | **The VC-Dimensions of Finite Automata with n States** - *Yoshiyasu Ishigami and Sei'ichi Tani* |

| | | **Monotonic Versus Non-monotonic Language Learning** - *S. Lange and T. Zeugmann* |

| | | **SIA: A Supervised Inductive Algorithm with Genetic Search for Learning Attributes based Concepts** - *Gilles Venturini* |

| | | **FOIL: A Midterm Report** - *J. Ross Quinlan and R. Mike Cameron-Jones* |

| | | **Worst-case quadratic loss bounds for a generalization of the Widrow-Hoff rule** - *N. Cesa-Bianchi, P. Long and M. Warmuth* |

| | | **Capabilities of probabilistic learners with bounded mind changes** - *R. Daley and B. Kalyanasundaram* |

| | | **On the power of inductive inference from good examples** - *R. Freivalds, E. B. Kinber and R. Wiehagen* |

| | | **On the average tractability of binary integer programming and the curious transition to perfect generalization in learning majority functions** - *S. Fang and S. Venkatesh* |

| | | **Learning in the presence of malicious errors** - *M. Kearns and M. Li* |

| | | **Synthesis of UNIX Programs Using Derivational Analogy** - *Sanjay Bhansali and Mehdi T. Harandi* |

| | | **How to invent characterizable inference methods for regular languages** - *Timo Knuutila* |

| | | **Synthesis of real time acceptors** - *Amr F. Fahmy and A. W. Biermann* |

| | | **Properties of Language Classes with Finite Elasticity** - *Takashi Moriyama and Masako Sato* |

| | | **Optimal layered learning: a PAC approach to incremental sampling** - *Stephen Muggleton* |

| | | **On learning systolic languages** - *Takashi Yokomori* |

| | | **Research Note on Decision Lists** - *Ron Kohavi and Scott Benson* |

| | | **On the Sample Complexity of Consistent Learning with One-Sided Error** - *Eiji Takimoto and Akira Maruoka* |

| | | **Effective Learning in Dynamic Environments by Explicit Context Tracking** - *Gerhard Widmer and Miroslav Kubat* |

| | | **Information Filtering: Selection Mechanisms in Learning Systems** - *Shaul Markovitch and Paul D. Scott* |

| | | **A Decomposition Based Induction Model for Discovering Concept Clusters from Databases** - *Ning Zhong and Setsuo Ohsuga* |

| | | **An Introduction to Kolmogorov Complexity and Its Applications** - *M. Li and P. Vitányi* |

| | | **A New Algorithm for Automatic Configuration of Hidden Markov Models** - *Makoto Iwayama, Nitin Indurkhya and Hiroshi Motoda* |

| | | **Inductive Logic Programming: Derivations, Successes and Shortcomings** - *Stephen Muggleton* |

| | | **Erratum to Discovery** - * Authorless* |

| | | **Efficient identification of regular expressions from representative examples** - *A. Brāzma* |

| | | **On learning embedded symmetric concepts** - *A. Blum, P. Chalasani and J. Jackson* |

| | | **Towards efficient inductive synthesis: Rapid construction of local regularities** - *J. Barzdins and G. Barzdins* |

| | | **Learning two-tape automata from queries and counterexamples** - *T. Yokomori* |

| | | **Probability is more powerful than team for language identification from positive data** - *S. Jain and A. Sharma* |

| | | **Acceleration of learning in binary choice problems** - *Y. Kabashima and S. Shinomoto* |

| | | **On-line learning of functions of bounded variation under various sampling schemes** - *S. E. Posner and S. R. Kulkarni* |

| | | **Lower bounds for PAC learning with queries** - *G. Turán* |

| | | **Algorithmisches Lernen von Funktionen und Sprachen** - *Thomas Zeugmann* |

| | | **A note on the query complexity of learning DFA** - *José L. Balcázar, Josep Díaz, Ricard Gavaldà and Osamu Watanabe* |

| | | **Choosing a reliable hypothesis** - *W. Evans, S. Rajagopalan and U. Vazirani* |

| | | **Learning With Growing Quality** - *Juris Viksna* |

| | | **On the role of procrastination for machine learning** - *R. Freivalds and C. H. Smith* |

| | | **Learning k-term monotone Boolean formulae** - *Yoshifumi Sakai and Akira Maruoka* |

| | | **Learning k ***mu* decision trees on the uniform distribution - *T. Hancock* |

| | | **Derivational Analogy in Prodigy: Automating Case Acquisition, Storage, and Utilization** - *Manuela M. Veloso and Jaime G. Carbonell* |

| | | **Inclusion is Undecidable for Pattern Languages** - *Tao Jiang, Arto Salomaa, Kai Salomaa and Sheng Yu* |

| | | **Implementation of heuristic problem solving process including analogical reasoning** - *Kazuhiro Ueda and Saburo Nagano* |

| | | **The stastical mechanics of learning a rule** - *T. L. H. Watkin, A. Rau and M. Biehl* |

| | | **Efficient learning of typical finite automata from random walks** - *Y. Freund, M. Kearns, D. Ron, R. Rubinfeld, R. Schapire and L. Sellie* |

| | | **An Analysis of the WITT Algorithm** - *Jan L. Talmon, Herco Fonteijn and Peter J. Braspenning* |

| | | **Learnability of Recursive, Non-determinate Theories: Some Basic Results and Techniques** - *M. Frazier and C. D. Page* |

| | | **The minimum consistent DFA problem cannot be approximated within any polynomial** - *Leonard Pitt and Manfred K. Warmuth* |

| | | **Learning Theory Toward Genome Informatics** - *Satoru Miyano* |

| | | **Inferential Theory of Learning as a Conceptual Basis for Multistrategy Learning** - *Ryszard S. Michalski* |

| | | **Rate of approximation results motivated by robust neural network learning** - *C. Darken, M. Donahue, L. Gurvits and E. Sontag* |

| | | **On the Learnability of Disjunctive Normal Form Formulas and Decision Trees.** - *H. Aizenstein* |

| | | **Some improved sample complexity bounds in the probabilistic PAC learning model** - *Jun-ichi Takeuchi* |

| | | **Iterative weighted least squares algorithms for neural networks classifiers** - *Takio Kurita* |

| | | **Can Complexity Theory Benefit from Learning Theory?** - *Tibor Hegedüs* |

| | | **Average case analysis of the clipped Hebb rule for nonoverlapping perceptron networks** - *M. Golea and M. Marchand* |

| | | **What Makes a Problem Hard for a Genetic Algorithm? Some Anomalous Results and Their Explanation** - *Stephanie Forrest and Melanie Mitchell* |

| | | **A Note on Refinement Operators** - *Tim Niblett* |

| | | **Machine Learning: A Multistrategy Approach** - *Ryszard Michalski and George Tecuci* |

| | | **Competitive learning by entropy minimization** - *Ryotaro Kamimura* |

| | | **Plausible Justification Trees: A Framework for Deep and Dynamic Integration of Learning Strategies** - *Gheorghe Tecuci* |

| | | **The Probably Approximately Correct (PAC) and Other Learning Models** - *D. Haussler and M. K. Warmuth* |

| | | **Regularization learning of neural networks for generalization** - *Shotaro Akaho* |

| | | **Explanation-Based Learning for Diagnosis** - *Yousri El Fattah and Paul O'Rorke* |

| | | **On PAC learnability of functional dependencies** - *Tatsuya Akutsu and Atsuhiro Takasu* |

| | | **Very Simple Classification Rules Perform Well on Most Commonly Used Datasets** - *Robert C. Holte* |

| | | **Induction of Probabilistic Rules Based on Rough Set Theory** - *Shusaka Tsumoto and Hiroshi Tanaka* |

| | | **Learning with the Knowledge of an Upper Bound on Program Size** - *S. Jain and A. Sharma* |

| | | **Learning Domain Theories using Abstract Beckground Knowledge** - *Peter Clark and Stan Matwin* |

| | | **Multi-Agent Learning: Theoretical and Empirical Studies** - *Robert Daley* |

| | | **Capabilities of fallible FINite learning** - *R. Daley, B. Kalyanasundaram and M. Velauthapillai* |

| | | **Introduction: Cognitive Autonomy in Machine Discovery** - *Jan M. Żytkow* |

| | | **Conservativeness and monotonicity for learning algorithms** - *E. Takimoto and A. Maruoka* |

| | | **Using Heuristics to Speed up Induction on Continuous-Valued Attributes** - *G. Seidelmann* |

| | | **Integrating Models of Knowledge and Machine Learning** - *Jean-Gabriel Ganascia, J. Thomas and Philippe Laublet* |

| | | **Learning with restricted focus of attention** - *S. Ben-David and E. Dichterman* |

| | | **On-line learning of rectangles in noisy environments** - *P. Auer* |

| | | **Unifying Learning Methods by Colored Digraphs** - *Kenichi Yoshida, Hiroshi Motoda and Nitin Indurkhya* |

| | | **Genetic Reinforcement Learning for Neurocontrol Problems** - *D. Whitley, S. Dominic, R. Das and C. W. Anderson* |

| | | **A Knowledge-Intensive Genetic Algorithm for Supervised Learning** - *Cezary Z. Janikow* |

| | | **The Learnability of Recursive Languages in Dependence on the Hypothesis Space** - *S. Lange and T. Zeugmann* |

| | | **Thue Systems and DNA - A Learning Algorithm for a Subclass** - *Rani Siromoney, D. G. Thomas, K. G. Subramanian and V. R. Dare* |

| | | **Complexity Dimensions and Learnability** - *Shan-Hwei Nienhuys-Cheng and M. Polman* |

| | | **Neural Discriminant Analysis** - *Jorge Ricardo Cuellar and Hans Ulrich Simon* |

| | | **Active Learning Using Arbitrary Binary Valued Queries** - *S. R. Kulkarni, S. K. Mitter and J. N. Tsitsiklis* |

| | | **Statistical queries and faulty PAC oracles** - *S. E. Decatur* |

| | | **Induction Over the Unexplained: Using Overly-General Domain Theories to Aid Concept Learning** - *Raymond J. Mooney* |

| | | **A Perceptual Criterion for Visually Controlling Learning** - *Masaki Suwa and Hiroshi Motoda* |

| | | **Domains of attraction in autoassociative memory networks for character pattern recognition** - *Koichi Niijima* |

| | | **The `lob-pass' problem and an on-line learning model of rational choice** - *N. Abe and J. Takeuchi* |

| | | **On the power of polynomial discriminators and radial basis function networks** - *M. Anthony and S. Holden* |

| | | **Opportunism and Learning** - *K. Hammond, T. Converse, M. Marks and C. M. Seifert* |

| | | **Learning ***mu*-branching programs with queries - *V. Raghavan and D. Wilkins* |

| | | **Amplification of weak learning under the uniform distribution** - *D. Boneh and R. Lipton* |

| | | **Polynomial learnability of linear threshold approximations** - *T. Bylander* |

| | | **Cost-Sensitive Learning of Classification Knowledge and Its Applications in Robotics** - *Ming Tan* |

| | | **Inductive inference with bounded mind changes** - *Yasuhito Mukouchi* |

| | | **Monotonic language learning** - *Shyam Kapur* |

| | | **Predicate Invention in ILP - an Overview** - *Irene Stahl* |

| | | **Some Lower Bounds for the Computational Complexity of Inductive Logic Programming** - *Jörg-Uwe Kietz* |

| | | **Use of Reduction Arguments in Determining Popperian FIN-Type Learning Capabilities** - *Robert Daley and Bala Kalyanasundaram* |

| | | **The subset principle is an intensional principle** - *K. Wexler* |

| | | **Using Genetic Algorithms for Concept Learning** - *Kenneth A. De Jong, William M. Spears and Diana F. Gordon* |

| | | **Indexing and Elaboration and Refinement: Incremental Learning of Explanatory Cases** - *Ashwin Ram* |

| | | **Genetic algorithms and machine learning** - *J. Grefenstette* |

| | | **Exact learning via the monotone theory** - *Nader H. Bshouty* |

| | | **Wastewater Treatment Systems from Case-Based Reasoning** - *Srinivas Krovvidy and William G. Wee* |

| | | **Induction of Recursive Bayesian Classifiers** - *Pat Langley* |

| | | **Lower bounds on the Vapnik-Chervonenkis dimension of multi-layer threshold networks** - *P. Bartlett* |

| | | **Protein secondary structure prediction based on stochastic-rule learning** - *Hiroshi Mamitsuka and Kenji Yamanishi* |

| | | **Experience Selection and Problem Choice in an Exploratory Learning System** - *Paul D. Scott and Shaul Markovitch* |

| | | **COBBIT - A Control Procedure for COBWEB in the Presence of Concept Drift** - *Fredrik Kilander and Carl Gustaf Jansson* |

| | | **Some computational lower bounds for the computational complexity of inductive logic programmming** - *Jorg-Uwe Kietz* |

| | | **Getting Order Independence in Incremental Learning** - *Antoine Cornuéjols* |

| | | **Rule Combination in Inductive Learning** - *L. Torgo* |

| | | **Discovery as Autonomous Learning from the Environment** - *Wei-Min Shen* |

| | | **Bivariate Scientific Function Finding in a Sampled, Real-Data Testbed** - *Cullen Schaffer* |

| | | **SAMIA: A Bottom-Up Learning Method Using a Simulated Annealing Algorithm** - *Pierre Brézelle and Henri Soldano* |

| | | **A Reply to Hellerstein's Book Review of Machine Learning: A Theoretical Approach** - *B. K. Natarajan* |

| | | **An Integrated Framework for Empirical Discovery** - *Bernd Nordhausen and Pat Langley* |

| | | **Learning Recursive Languages With a Bounded Number of Mind Changes** - *S. Lange and T. Zeugmann* |

| | | **The Design of Discrimination Experiments** - *Shankar A. Rajamoney* |

| | | **Controlled Redundancy in Incremental Rule Learning** - *L. Torgo* |

| | | **Coding Decision Trees** - *C. S. Wallace and J. D. Patrick* |

| | | **Exploiting Context When Learning to Classify** - *Peter D. Turney* |

| | | **Can complexity theory benefit from learning theory? (Extended Abstract)** - *T. Hegedűs* |

| | | **An on-line algorithm for improving performance in navigation.** - *A. Blum and P. Chalasani* |

| | | **How to use expert advice** - *N. Cesa-Bianchi, Y. Freund, D. P. Helmbold, D. Haussler, R. E. Schapire and M. K. Warmuth* |

| | | **Machine Discovery of Effective Admissible Heuristics** - *Armand E. Prieditis* |

| | | **Learnability of Constrained Logic Programs** - *Saso Dzeroski, Stephen Muggleton and Stuart J. Russell* |

| | | **Book Review: Machine Learning: A Theoretical Approach** - *Lisa Hellerstein* |

| | | **An Iterative and Bottom-up Procedure for Proving-by-Example** - *Masami Hagiya* |

| | | **Keeping neural networks simple by minimizing the description length of the weights** - *G. Hinton and D. van Camp* |

| | | **Balanced Cooperative Modeling** - *Katharina Morik* |

| | | **Design Methods for Scientific Hypothesis Formation and Their Application to Molecular Biology** - *Peter D. Karp* |

| | | **An application of Bernstein polynomials in PAC model** - *Masahiro Matsuoka* |

| | | **Inductive Inference Machines That Can Refute Hypothesis Spaces** - *Yasuhito Mukouchi and Setsuo Arikawa* |

| | | **Using Knowledge-Based Neural Networks to Improve Algorithms: Refining the Chou-Fasman Algorithm for Protein Folding** - *Richard Maclin and Jude W. Shavlik* |

| | | **Overfitting Avoidance as Bias** - *Cullen Schaffer* |

| January | | **Information bounds for the risk of Bayesian predictions and the redundancy of universal codes** - *A. Barron, B. Clarke and D. Haussler* |

| | | **Searching in an Unknown Environment: An Optimal Randomized Algorithm for the Cow-Path Problem** - *M. Kao, J. H. Reif and S. R. Tate* |

| February | | **Pattern Recognition and Valiant's Learning Framework** - *L. Saitta and F. Bergadano* |

| April | | **Inference of Finite Automata using Homing Sequences** - *R. L. Rivest and R. E. Schapire* |

| June | | **Learning from entailment: An application to propositional Horn sentences** - *Michael Frazier and Leonard Pitt* |

| July | | **Using Dirichlet Mixture Priors to Derive Hidden Markov Models for Protein Families** - *M. P. Brown, R. Hughey, A. Krogh, I. S. Mian, K. Sjölander and D. Haussler* |

| August | | **Exact identification of circuits using fixed points of amplification functions** - *S. A. Goldman, M. J. Kearns and R. E. Schapire* |

| October | | **Algorithmic Learning Theory, Third Workshop, ALT '92, Tokyo, Japan, October 1992, Proceedings** - *S. Doshita and K. Furukawa and K. P. Jantke and T. Nishida* |

| | | **Learning binary relations and total orders** - *S. A. Goldman, R. L. Rivest and R. E. Schapire* |

| November | | **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* |