Efficient Range Partitioning in Classification Learning
暂无分享,去创建一个
[1] J. Ross Quinlan,et al. C4.5: Programs for Machine Learning , 1992 .
[2] Ian H. Witten,et al. Text Compression , 1990, 125 Problems in Text Algorithms.
[3] C. Brodley,et al. On the Qualitative Behavior of Impurity-Based Splitting Rules I: The Minima-Free Property , 1997 .
[4] Marco Richeldi,et al. Class-Driven Statistical Discretization of Continuous Attributes (Extended Abstract) , 1995, ECML.
[5] Xindong Wu,et al. A Bayesian Discretizer for Real-Valued Attributes , 1996, Comput. J..
[6] C. S. Wallace,et al. Estimation and Inference by Compact Coding , 1987 .
[7] Thomas G. Dietterich,et al. Bootstrap Methods for the Cost-Sensitive Evaluation of Classifiers , 2000, ICML.
[8] Paul E. Utgoff,et al. Classification Using Φ-Machines and Constructive Function Approximation , 1998, Machine Learning.
[9] Igor Kononenko,et al. ReliefF for estimation and discretization of attributes in classification, regression, and ILP probl , 1996 .
[10] Catherine Blake,et al. UCI Repository of machine learning databases , 1998 .
[11] Usama M. Fayyad,et al. The Attribute Selection Problem in Decision Tree Generation , 1992, AAAI.
[12] Kai Ming Ting,et al. Discretization of Continuous-Valued Attributes and Instance-Based Learning , 1994 .
[13] C. S. Wallace,et al. Coding Decision Trees , 1993, Machine Learning.
[14] Randy Kerber,et al. ChiMerge: Discretization of Numeric Attributes , 1992, AAAI.
[15] Nello Cristianini,et al. Data-Dependent Structural Risk Minimization for Perceptron Decision Trees , 1997, NIPS.
[16] Jason Catlett,et al. On Changing Continuous Attributes into Ordered Discrete Attributes , 1991, EWSL.
[17] Simon Kasif,et al. OC1: A Randomized Induction of Oblique Decision Trees , 1993, AAAI.
[18] Jukka Hekanaho. DOGMA: A GA-Based Relational Learner , 1998, ILP.
[19] Nello Cristianini,et al. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .
[20] J. Ross Quinlan,et al. Simplifying Decision Trees , 1987, Int. J. Man Mach. Stud..
[21] Geoffrey E. Hinton,et al. Learning internal representations by error propagation , 1986 .
[22] J. Ross Quinlan,et al. Induction of Decision Trees , 1986, Machine Learning.
[23] Nir Friedman,et al. Bayesian Network Classification with Continuous Attributes: Getting the Best of Both Discretization and Parametric Fitting , 1998, ICML.
[24] David Haussler,et al. Decision Theoretic Generalizations of the PAC Model for Neural Net and Other Learning Applications , 1992, Inf. Comput..
[25] Michael J. Pazzani,et al. An Iterative Improvement Approach for the Discretization of Numeric Attributes in Bayesian Classifiers , 1995, KDD.
[26] J. Kent Martin,et al. An Exact Probability Metric for Decision Tree Splitting and Stopping , 1997, Machine Learning.
[27] Peter Clark,et al. The CN2 induction algorithm , 2004, Machine Learning.
[28] Nir Friedman,et al. Discretizing Continuous Attributes While Learning Bayesian Networks , 1996, ICML.
[29] Steven Salzberg,et al. Lookahead and Pathology in Decision Tree Induction , 1995, IJCAI.
[30] Carla E. Brodley,et al. Multivariate decision trees , 2004, Machine Learning.
[31] Paul D. Scott,et al. Zeta: A Global Method for Discretization of Continuous Variables , 1997, KDD.
[32] Glen G. Langdon,et al. Arithmetic Coding , 1979 .
[33] Ray J. Hickey,et al. Noise Modelling and Evaluating Learning from Examples , 1996, Artif. Intell..
[34] Ramón López de Mántaras,et al. A distance-based attribute selection measure for decision tree induction , 1991, Machine Learning.
[35] Changhwan Lee,et al. A Context-Sensitive Discretization of Numeric Attributes for Classification Learning , 1994, ECAI.
[36] Igor Kononenko,et al. A counter example to the stronger version of the binarytree hypothesisIgor , 1995 .
[37] Huan Liu,et al. Feature Selection via Discretization , 1997, IEEE Trans. Knowl. Data Eng..
[38] Wei Zhong Liu,et al. Bias in information-based measures in decision tree induction , 1994, Machine Learning.
[39] Thomas M. Cover,et al. Elements of Information Theory , 2005 .
[40] Ramón López de Mántaras,et al. Proposal and Empirical Comparison of a Parallelizable Distance-Based Discretization Method , 1997, KDD.
[41] Jonathan R. M. Hosking,et al. Partitioning Nominal Attributes in Decision Trees , 1999, Data Mining and Knowledge Discovery.
[42] Djamel A. Zighed,et al. Optimal Multiple Intervals Discretization of Continuous Attributes for Supervised Learning , 1997, KDD.
[43] Ron Kohavi,et al. The Case against Accuracy Estimation for Comparing Induction Algorithms , 1998, ICML.
[44] Wei-Yin Loh,et al. Split Selection Methods for Classication Trees Published in Statistica Sinica, 1997, Vol. 7, pp. 815{840 , 1997 .
[45] Leslie G. Valiant,et al. A theory of the learnable , 1984, CACM.
[46] Teuvo Kohonen,et al. Self-organization and associative memory: 3rd edition , 1989 .
[47] Umesh V. Vazirani,et al. An Introduction to Computational Learning Theory , 1994 .
[48] Andreas Birkendorf. On Fast and Simple Algorithms for Finding Maximal Subarrays and Applications in Learning Theory , 1997, EuroCOLT.
[49] Jorma Rissanen,et al. Stochastic Complexity in Statistical Inquiry , 1989, World Scientific Series in Computer Science.
[50] Ivan Bratko,et al. Experiments in automatic learning of medical diagnostic rules , 1984 .
[51] 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.
[52] Judea Pearl,et al. Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.
[53] Bernhard Pfahringer,et al. Compression-Based Discretization of Continuous Attributes , 1995, ICML.
[54] Dana Angluin,et al. Computational learning theory: survey and selected bibliography , 1992, STOC '92.
[55] C. S. Wallace,et al. An Information Measure for Classification , 1968, Comput. J..
[56] Ron Kohavi,et al. Useful Feature Subsets and Rough Set Reducts , 1994 .
[57] J. Rissanen. A UNIVERSAL PRIOR FOR INTEGERS AND ESTIMATION BY MINIMUM DESCRIPTION LENGTH , 1983 .
[58] J. R. Quilan. Decision trees and multi-valued attributes , 1988 .
[59] Se June Hong,et al. Use of Contextaul Information for Feature Ranking and Discretization , 1997, IEEE Trans. Knowl. Data Eng..
[60] Ian H. Witten,et al. Using a Permutation Test for Attribute Selection in Decision Trees , 1998, ICML.
[61] Andrew K. C. Wong,et al. Class-Dependent Discretization for Inductive Learning from Continuous and Mixed-Mode Data , 1995, IEEE Trans. Pattern Anal. Mach. Intell..
[62] Gregory F. Cooper,et al. A latent variable model for multivariate discretization , 1999, AISTATS.
[63] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[64] Yishay Mansour,et al. A Fast, Bottom-Up Decision Tree Pruning Algorithm with Near-Optimal Generalization , 1998, ICML.
[65] Tapio Elomaa,et al. Tools and Techniques for Decision Tree Learning , 1996 .
[66] William W. Cohen. Fast Eeective Rule Induction , 1995 .
[67] Ronald L. Rivest,et al. Inferring Decision Trees Using the Minimum Description Length Principle , 1989, Inf. Comput..
[68] J.. Stochastic Complexity in Learning , .
[69] Peter Auer,et al. Theory and Applications of Agnostic PAC-Learning with Small Decision Trees , 1995, ICML.
[70] J. Praagman. Book reviewClassification and regression trees: Leo BREIMAN, Jerome H. FRIEDMAN, Richard A. OLSHEN and Charles J. STONE The Wadsworth Statistics/Probability Series, Wadsworth, Belmont, 1984, x + 358 pages , 1985 .
[71] Igor Kononenko,et al. On Biases in Estimating Multi-Valued Attributes , 1995, IJCAI.