Machine-Learning Research

Machine-learning research has been making great progress in many directions. This article summarizes four of these directions and discusses some current open problems. The four directions are (1) the improvement of classification accuracy by learning ensembles of classifiers, (2) methods for scaling up supervised learning algorithms, (3) reinforcement learning, and (4) the learning of complex stochastic models.

[1]  Ronald L. Rivest,et al.  Inferring Decision Trees Using the Minimum Description Length Principle , 1989, Inf. Comput..

[2]  Jason Catlett,et al.  On Changing Continuous Attributes into Ordered Discrete Attributes , 1991, EWSL.

[3]  Leslie Pack Kaelbling,et al.  Learning Policies for Partially Observable Environments: Scaling Up , 1997, ICML.

[4]  Wray L. Buntine.  A Guide to the Literature on Learning Probabilistic Networks from Data , 1996, IEEE Trans. Knowl. Data Eng..

[5]  Wray L. Buntine.  Operations for Learning with Graphical Models , 1994, J. Artif. Intell. Res..

[6]  Dimitri P. Bertsekas,et al.  Reinforcement Learning for Dynamic Channel Allocation in Cellular Telephone Systems , 1996, NIPS.

[7]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.

[8]  William W. Cohen.  Fast Effective Rule Induction , 1995, ICML.

[9]  Paul W. Munro,et al.  Improving Committee Diagnosis with Resampling Techniques , 1995, NIPS.

[10]  Kagan Tumer,et al.  Error Correlation and Error Reduction in Ensemble Classifiers , 1996, Connect. Sci..

[11]  Richard S. Sutton,et al.  Introduction to Reinforcement Learning , 1998 .

[12]  Thomas G. Dietterich,et al.  Error-Correcting Output Coding Corrects Bias and Variance , 1995, ICML.

[13]  Lars Kai Hansen,et al.  Neural Network Ensembles , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  J. Mesirov,et al.  Hybrid system for protein secondary structure prediction. , 1992, Journal of molecular biology.

[15]  David Maxwell Chickering,et al.  Learning Bayesian Networks: The Combination of Knowledge and Statistical Data , 1994, Machine Learning.

[16]  Geoffrey E. Hinton,et al.  Feudal Reinforcement Learning , 1992, NIPS.

[17]  Michael I. Jordan,et al.  Probabilistic Independence Networks for Hidden Markov Probability Models , 1997, Neural Computation.

[18]  Monte Zweben,et al.  Scheduling and rescheduling with iterative repair , 1993, IEEE Trans. Syst. Man Cybern..

[19]  Robert E. Schapire,et al.  Using output codes to boost multiclass learning problems , 1997, ICML.

[20]  Walter R. Gilks,et al.  A Language and Program for Complex Bayesian Modelling , 1994 .

[21]  C. N. Liu,et al.  Approximating discrete probability distributions with dependence trees , 1968, IEEE Trans. Inf. Theory.

[22]  Gregory F. Cooper,et al.  A Bayesian method for the induction of probabilistic networks from data , 1992, Machine Learning.

[23]  Ben J. A. Kröse,et al.  Learning from delayed rewards , 1995, Robotics Auton. Syst..

[24]  A. Atkinson.  Subset Selection in Regression , 1992 .

[25]  Paul W. Munro,et al.  Competition Among Networks Improves Committee Performance , 1996, NIPS.

[26]  David W. Opitz,et al.  Generating Accurate and Diverse Members of a Neural-Network Ensemble , 1995, NIPS.

[27]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  Yaser S. Abu-Mostafa,et al.  Learning from hints in neural networks , 1990, J. Complex..

[29]  Ron Kohavi,et al.  Option Decision Trees with Majority Votes , 1997, ICML.

[30]  David G. Lowe,et al.  Similarity Metric Learning for a Variable-Kernel Classifier , 1995, Neural Computation.

[31]  Geoffrey J. Gordon.  Stable Function Approximation in Dynamic Programming , 1995, ICML.

[32]  Andrew McCallum,et al.  Instance-Based Utile Distinctions for Reinforcement Learning with Hidden State , 1995, ICML.

[33]  Oren Etzioni,et al.  Explanation-Based Learning: A Problem Solving Perspective , 1989, Artif. Intell..

[34]  Judea Pearl,et al.  Equivalence and Synthesis of Causal Models , 1990, UAI.

[35]  Christopher Meek,et al.  Learning Bayesian Networks with Discrete Variables from Data , 1995, KDD.

[36]  Anton Schwartz,et al.  A Reinforcement Learning Method for Maximizing Undiscounted Rewards , 1993, ICML.

[37]  Dan Roth,et al.  Applying Winnow to Context-Sensitive Spelling Correction , 1996, ICML.

[38]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[39]  Leslie Pack Kaelbling,et al.  The National Science Foundation Workshop on Reinforcement Learning , 1996, AI Mag..

[40]  Andrew G. Barto,et al.  Improving Elevator Performance Using Reinforcement Learning , 1995, NIPS.

[41]  Stuart J. Russell,et al.  Stochastic simulation algorithms for dynamic probabilistic networks , 1995, UAI.

[42]  Thomas G. Dietterich,et al.  Solving Multiclass Learning Problems via Error-Correcting Output Codes , 1994, J. Artif. Intell. Res..

[43]  Marko Robnik-Sikonja,et al.  Overcoming the Myopia of Inductive Learning Algorithms with RELIEFF , 2004, Applied Intelligence.

[44]  Andrew G. Barto,et al.  Learning to Act Using Real-Time Dynamic Programming , 1995, Artif. Intell..

[45]  N. Littlestone.  Learning Quickly When Irrelevant Attributes Abound: A New Linear-Threshold Algorithm , 1987, 28th Annual Symposium on Foundations of Computer Science (sfcs 1987).

[46]  Allen Newell,et al.  SOAR: An Architecture for General Intelligence , 1987, Artif. Intell..

[47]  Pedro M. Domingos,et al.  Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier , 1996, ICML.

[48]  Chris Carter,et al.  Multiple decision trees , 2013, UAI.

[49]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.

[50]  Nir Friedman,et al.  Building Classifiers Using Bayesian Networks , 1996, AAAI/IAAI, Vol. 2.

[51]  R. Clemen.  Combining forecasts: A review and annotated bibliography , 1989 .

[52]  Rakesh Agrawal,et al.  SPRINT: A Scalable Parallel Classifier for Data Mining , 1996, VLDB.

[53]  Andrew W. Moore,et al.  Efficient Algorithms for Minimizing Cross Validation Error , 1994, ICML.

[54]  David J. Spiegelhalter,et al.  Bayesian analysis in expert systems , 1993 .

[55]  Prasad Tadepalli,et al.  Auto-Exploratory Average Reward Reinforcement Learning , 1996, AAAI/IAAI, Vol. 1.

[56]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[57]  Andrew W. Moore,et al.  Reinforcement Learning: A Survey , 1996, J. Artif. Intell. Res..

[58]  Larry A. Rendell,et al.  A Practical Approach to Feature Selection , 1992, ML.

[59]  Ron Kohavi,et al.  Irrelevant Features and the Subset Selection Problem , 1994, ICML.

[60]  Bruce E. Rosen,et al.  Ensemble Learning Using Decorrelated Neural Networks , 1996, Connect. Sci..

[61]  Matthew Self,et al.  Bayesian Classification , 1988, AAAI.

[62]  Sherif Hashem,et al.  Optimal Linear Combinations of Neural Networks , 1997, Neural Networks.

[63]  John N. Tsitsiklis,et al.  Neuro-Dynamic Programming , 1996, Encyclopedia of Machine Learning.

[64]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[65]  L. Cooper,et al.  When Networks Disagree: Ensemble Methods for Hybrid Neural Networks , 1992 .

[66]  Rich Caruana,et al.  Algorithms and Applications for Multitask Learning , 1996, ICML.

[67]  Keiji Kanazawa,et al.  A model for reasoning about persistence and causation , 1989 .

[68]  Igor Kononenko,et al.  Estimating Attributes: Analysis and Extensions of RELIEF , 1994, ECML.

[69]  Enrique F. Castillo,et al.  Expert Systems and Probabilistic Network Models , 1996, Monographs in Computer Science.

[70]  R. Bellman.  Dynamic programming. , 1957, Science.

[71]  Stuart J. Russell,et al.  Local Learning in Probabilistic Networks with Hidden Variables , 1995, IJCAI.

[72]  Usama M. Fayyad,et al.  Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning , 1993, IJCAI.

[73]  David W. Aha,et al.  A Review and Empirical Evaluation of Feature Weighting Methods for a Class of Lazy Learning Algorithms , 1997, Artificial Intelligence Review.

[74]  David J. C. MacKay,et al.  A Practical Bayesian Framework for Backpropagation Networks , 1992, Neural Computation.

[75]  Ronald L. Rivest,et al.  Constructing Optimal Binary Decision Trees is NP-Complete , 1976, Inf. Process. Lett..

[76]  Nathan Intrator,et al.  Bootstrapping with Noise: An Effective Regularization Technique , 1996, Connect. Sci..

[77]  Geoffrey E. Hinton,et al.  Using Generative Models for Handwritten Digit Recognition , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[78]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[79]  Salvatore J. Stolfo,et al.  Learning Arbiter and Combiner Trees from Partitioned Data for Scaling Machine Learning , 1995, KDD.

[80]  Leslie Pack Kaelbling,et al.  Acting Optimally in Partially Observable Stochastic Domains , 1994, AAAI.

[81]  Christopher J. Merz,et al.  UCI Repository of Machine Learning Databases , 1996 .

[82]  J. Ross Quinlan,et al.  Bagging, Boosting, and C4.5 , 1996, AAAI/IAAI, Vol. 1.

[83]  Peter Dayan,et al.  Technical Note: Q-Learning , 2004, Machine Learning.

[84]  John F. Kolen,et al.  Backpropagation is Sensitive to Initial Conditions , 1990, Complex Syst..

[85]  Andreas Stolcke,et al.  Best-first Model Merging for Hidden Markov Model Induction , 1994, ArXiv.

[86]  Wei Zhang,et al.  A Reinforcement Learning Approach to job-shop Scheduling , 1995, IJCAI.

[87]  Jorma Rissanen,et al.  SLIQ: A Fast Scalable Classifier for Data Mining , 1996, EDBT.

[88]  Finn Verner Jensen,et al.  Introduction to Bayesian Networks , 2008, Innovations in Bayesian Networks.

[89]  Gerald Tesauro,et al.  Practical Issues in Temporal Difference Learning , 1992, Mach. Learn..

[90]  Michael I. Jordan,et al.  Hierarchical Mixtures of Experts and the EM Algorithm , 1994, Neural Computation.

[91]  Gerald Tesauro,et al.  Temporal difference learning and TD-Gammon , 1995, CACM.

[92]  Bruce D'Ambrosio,et al.  Incremental Probabilistic Inference , 1993, UAI.

[93]  Johannes Fürnkranz,et al.  Incremental Reduced Error Pruning , 1994, ICML.

[94]  P. Spirtes,et al.  Causation, prediction, and search , 1993 .

[95]  Stuart J. Russell,et al.  Approximating Optimal Policies for Partially Observable Stochastic Domains , 1995, IJCAI.

[96]  H. Kucera,et al.  Computational analysis of present-day American English , 1967 .