Multistrategy constructive induction

ion Arithmetic Logical Valuegrouping Rulegrouping Representation Space Search Operators Multi-valued Binary Conditiongrouping Data-driven Attributeremoving Hypothesis-driven

[1]  Simon Kasif,et al.  Efficient Algorithms for Finding Multi-way Splits for Decision Trees , 1995, ICML.

[2]  William W. Cohen.  An Analysis of Representation Shift in Concept Learning , 1990, ML.

[3]  João Gama,et al.  Characterizing the Applicability of Classification Algorithms Using Meta-Level Learning , 1994, ECML.

[4]  Stefan Kramer.  CN2-MCI: A Two-Step Method for Constructive Induction , 1994 .

[5]  Miroslav Kubat.  Second Tier for Decision Trees , 1996, ICML.

[6]  Larry A. Rendell,et al.  Opportunistic Constructive Induction , 1991, ML.

[7]  Larry A. Rendell,et al.  The Feature Selection Problem: Traditional Methods and a New Algorithm , 1992, AAAI.

[8]  Stan Matwin,et al.  A General Criterion for Measuring Quality of Concept Descriptions , 1988 .

[9]  J. Ross Quinlan,et al.  Improved Use of Continuous Attributes in C4.5 , 1996, J. Artif. Intell. Res..

[10]  Bernhard Pfahringer,et al.  Compression-Based Discretization of Continuous Attributes , 1995, ICML.

[11]  Rich Caruana,et al.  Multitask Learning: A Knowledge-Based Source of Inductive Bias , 1993, ICML.

[12]  W. Spears,et al.  For Every Generalization Action, Is There Really an Equal and Opposite Reaction? , 1995, ICML.

[13]  Cullen Schaffer,et al.  A Conservation Law for Generalization Performance , 1994, ICML.

[14]  Ryszard S. Michalski,et al.  A theory and methodology of inductive learning , 1993 .

[15]  Lyle H. Ungar,et al.  Inventing theoretical terms in inductive learning , 1989 .

[16]  Leonid V. Belyaev,et al.  Noise-Resistant Classification , 1991, ML.

[17]  Ryszard S. Michalski,et al.  Inferential Learning Theory as a Basis for Multistrategy Task-Adaptive Learning , 1991 .

[18]  Richard S. Sutton,et al.  Learning Polynomial Functions by Feature Construction , 1991, ML.

[19]  Carla E. Brodley,et al.  Addressing the Selective Superiority Problem: Automatic Algorithm/Model Class Selection , 1993 .

[20]  Nada Lavrac,et al.  The Multi-Purpose Incremental Learning System AQ15 and Its Testing Application to Three Medical Domains , 1986, AAAI.

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

[22]  Larry A. Rendell,et al.  Using Multidimensional Projection to Find Relations , 1995, ICML.

[23]  Randy Kerber,et al.  ChiMerge: Discretization of Numeric Attributes , 1992, AAAI.

[24]  H. Simon,et al.  Rediscovering Chemistry with the Bacon System , 1983 .

[25]  Ryszard S. Michalski,et al.  Pattern Recognition as Knowledge-Guided Computer Induction , 1978 .

[26]  Janusz Wnek,et al.  Hypothesis-driven constructive induction , 1993 .

[27]  Ryszard S. Michalski,et al.  The AQ17-DCI System for Data-Driven Constructive Induction and its Application to the Analysis of World Economics , 1996, ISMIS.

[28]  Martin Stacey,et al.  Scientific Discovery: Computational Explorations of the Creative Processes , 1988 .

[29]  G. Kelly.  The Psychology of Personal Constructs , 2020 .

[30]  Pankaj Mehra,et al.  Constructive Induction Framework , 1989, ML Workshop.

[31]  John Foster Provost,et al.  Policies for the selection of bias in inductive machine learning , 1992 .

[32]  Stan Matwin,et al.  Explanation-Based Learning with Incomplete Theories: A Three-step Approach , 1990, ML.

[33]  Benjamin W. Wah,et al.  Principled Constructive Induction , 1989, IJCAI.

[34]  Ryszard S. Michalski,et al.  CONSTRUCTIVE INDUCTION FROM DATA IN AQ17-DCI: Further Experiments , 1991 .

[35]  Yuh-Jyh Hu,et al.  Generation of Attributes for Learning Algorithms , 1996, AAAI/IAAI, Vol. 1.

[36]  Ryszard S. Michalski,et al.  Inferential Theory of Learning: Developing Foundations for Multistrategy Learning , 1992 .

[37]  Ryszard S. Michalski,et al.  Two-tiered concept meaning, inferential matching, and conceptual cohesiveness , 1989 .

[38]  Diana Faye Gordon.  Active bias adjustment for incremental, supervised concept learning , 1990 .

[39]  Ranan B. Banerji,et al.  Learning in the Limit in a Growing Language , 1987, IJCAI.

[40]  Paul E. Utgoff,et al.  Shift of bias for inductive concept learning , 1984 .

[41]  Larry A. Rendell,et al.  Learning hard concepts through constructive induction: framework and rationale , 1990, Comput. Intell..

[42]  Sebastian Thrun,et al.  Discovering Structure in Multiple Learning Tasks: The TC Algorithm , 1996, ICML.

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

[44]  Sholom M. Weiss,et al.  Computer Systems That Learn , 1990 .

[45]  Larry A. Rendell,et al.  Lookahead Feature Construction for Learning Hard Concepts , 1993, International Conference on Machine Learning.

[46]  David Tcheng,et al.  MORE ROBUST CONCEPT LEARNING USING DYNAMICALLY – VARIABLE BIAS , 1987 .

[47]  Yves Kodratoff.  Proceedings of the European working session on learning on Machine learning , 1991 .

[48]  Pankaj Mehra,et al.  Constructing representations using inverted spaces , 1989, ICML 1989.

[49]  Shogo Nishida,et al.  Learning to Learn Decision Trees , 1992, AAAI.

[50]  David W. Aha,et al.  Generalizing from Case studies: A Case Study , 1992, ML.

[51]  Gheorghe Tecuci,et al.  Apprenticeship learning in imperfect domain theories , 1990 .

[52]  George Drastal,et al.  Induction in an Abstraction Space: A Form of Constructive Induction , 1989, IJCAI.

[53]  Janusz Wnek,et al.  DIAV 2.0 User Manual: Specification and Guide through the Diagrammatic Visualization System , 1995 .

[54]  Jerzy W. Bala,et al.  Learning to recognize visual concepts: Development and implementation of a method for texture concep , 1993 .

[55]  Ryszard S. Michalski,et al.  Learning as Goal-Driven Inference , 1995 .

[56]  Larry A. Rendell,et al.  A Scheme for Feature Construction and a Comparison of Empirical Methods , 1991, IJCAI.

[57]  Renée Elio,et al.  Guiding Constructive Induction for Incremental Learning from Examples , 1987, IJCAI.

[58]  Ryszard S. Michalski,et al.  Constructive Closed-Loop Learning: Fundamental Ideas and Examples , 1988 .

[59]  Katharina Morik,et al.  Knowledge Representation and Organization in Machine Learning , 1989, Lecture Notes in Computer Science.

[60]  David J. Spiegelhalter,et al.  Machine Learning, Neural and Statistical Classification , 2009 .

[61]  Ron Kohavi,et al.  Supervised and Unsupervised Discretization of Continuous Features , 1995, ICML.

[62]  Huan Liu,et al.  A Probabilistic Approach to Feature Selection - A Filter Solution , 1996, ICML.

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

[64]  Ryszard S. Michalski,et al.  Toward a Unified Theory of Learning: An Outline of Basic Ideas , 1991 .

[65]  Larry A. Rendell,et al.  Constructive Induction On Decision Trees , 1989, IJCAI.

[66]  Larry A. Rendell,et al.  Constructive Induction Using Fragmentary Knowledge , 1996, ICML.

[67]  Christopher J. Matheus,et al.  Adding Domain Knowledge to SBL Through Feature Construction , 1990, AAAI.

[68]  Chris Tong,et al.  Automatic Construction of a Hierarchical Generate-and-Test Algorithm , 1989, ML.

[69]  Daphne Koller,et al.  Toward Optimal Feature Selection , 1996, ICML.

[70]  Larry A. Rendell,et al.  Feature construction: an analytic framework and an application to decision trees , 1990 .

[71]  Jan M. Zytkow,et al.  Automated Discovery of Empirical Equations from Data , 1991, ISMIS.

[72]  Geoffrey I. Webb,et al.  Incorporating canonical discriminant attributes in classification learning , 1994 .

[73]  Stefan Wrobel.  Demand-Driven Concept Formation , 1987, Knowledge Representation and Organization in Machine Learning.

[74]  Mieczyslaw M. Kokar,et al.  Discovering Functional Formulas through Changing Representation Base , 1986, AAAI.

[75]  Renée Elio,et al.  An incremental deductive strategy for controlling constructive induction in learning from examples , 1991, Machine Learning.

[76]  Paul E. Utgoff,et al.  Constructive Induction on Domain Information , 1991, AAAI.

[77]  Kenneth A. Kaufman,et al.  Data-Driven Constructive Induction in INLEN , 1996 .

[78]  Ryszard S. Michalski,et al.  AQ15: Incremental Learning of Attribute-Based Descriptions from Examples: The Method and User's Guide , 1986 .

[79]  Larry A. Rendell,et al.  Rerepresenting and Restructuring Domain Theories: A Constructive Induction Approach , 1994, J. Artif. Intell. Res..

[80]  Kenneth A. Kaufman.  Comparing International Development Patterns Using Multi-Operator Learning and Discovery Tools , 1994, KDD Workshop.

[81]  J. C. Schlimmer,et al.  Concept acquisition through representational adjustment , 1987 .