Reliable Reasoning: Induction and Statistical Learning Theory

In Reliable Reasoning, Gilbert Harman and Sanjeev Kulkarni -- a philosopher and an engineer -- argue that philosophy and cognitive science can benefit from statistical learning theory (SLT), the theory that lies behind recent advances in machine learning. The philosophical problem of induction, for example, is in part about the reliability of inductive reasoning, where the reliability of a method is measured by its statistically expected percentage of errors -- a central topic in SLT. After discussing philosophical attempts to evade the problem of induction, Harman and Kulkarni provide an admirably clear account of the basic framework of SLT and its implications for inductive reasoning. They explain the Vapnik-Chervonenkis (VC) dimension of a set of hypotheses and distinguish two kinds of inductive reasoning. The authors discuss various topics in machine learning, including nearest-neighbor methods, neural networks, and support vector machines. Finally, they describe transductive reasoning and suggest possible new models of human reasoning suggested by developments in SLT.

[1]  Vladimir Vapnik,et al.  Chervonenkis: On the uniform convergence of relative frequencies of events to their probabilities , 1971 .

[2]  G. Harman,et al.  Enumerative Induction as Inference to the Best Explanation , 1968 .

[3]  David G. Stork,et al.  Pattern Classification , 1973 .

[4]  K. Popper.  Objective Knowledge: An Evolutionary Approach , 1972 .

[5]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory, Second Edition , 2000, Statistics for Engineering and Information Science.

[6]  M. F.,et al.  Bibliography , 1985, Experimental Gerontology.

[7]  A. Walsh,et al.  Moral Particularism , 2003 .

[8]  Michael A. Bishop,et al.  Epistemology and the Psychology of Human Judgment , 2004 .

[9]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[10]  David Chiu,et al.  BOOK REVIEW: "PATTERN CLASSIFICATION", R. O. DUDA, P. E. HART and D. G. STORK, Second Edition , 2001 .

[11]  G. Spielthenner,et al.  Moral Reasons , 2008 .

[12]  J. Rissanen,et al.  Modeling By Shortest Data Description* , 1978, Autom..

[13]  H. Akaike.  A new look at the statistical model identification , 1974 .

[14]  Norman Daniels,et al.  Wide Reflective Equilibrium and Theory Acceptance in Ethics , 1979 .

[15]  A. Tversky,et al.  Judgment under Uncertainty: Heuristics and Biases , 1974, Science.

[16]  Peter W. Culicover,et al.  Principles and Parameters: An Introduction to Syntactic Theory , 1997 .

[17]  Spike Cramphorn.  Blink: The Power of Thinking without Thinking / Strangers to Ourselves: Discovering the Adaptive Unconscious , 2006, Journal of Advertising Research.

[18]  E. Mark Gold,et al.  Language Identification in the Limit , 1967, Inf. Control..

[19]  Pekka Väyrynen,et al.  Particularism and Default Reasons , 2004 .

[20]  Ray J. Solomonoff,et al.  A Formal Theory of Inductive Inference. Part II , 1964, Inf. Control..

[21]  S. Harnad.  Psychophysical and cognitive aspects of categorical perception: A critical overview , 1987 .

[22]  R. Swinburne.  OBJECTIVE KNOWLEDGE: AN EVOLUTIONARY APPROACH , 1973 .

[23]  Noam Chomsky,et al.  Lectures on Government and Binding , 1981 .

[24]  Dan Simon,et al.  Constraint Satisfaction Processes in Social Reasoning , 2003 .

[25]  John McDowell,et al.  Mind, Value, and Reality , 1998 .

[26]  K J Holyoak,et al.  The Emergence of Coherence Over the Course of Decision Making , 2004 .

[27]  John Field,et al.  Language and the mind , 1968 .

[28]  M. Lepper,et al.  The Construction of Preference: When Choice Is Demotivating: Can One Desire Too Much of a Good Thing? , 2006 .

[29]  Ray J. Solomonoff,et al.  A Formal Theory of Inductive Inference. Part I , 1964, Inf. Control..

[30]  D. Fara.  Shifting sands: An interest relative theory of vagueness , 2000 .

[31]  Gregory J. Chaitin,et al.  Information-Theoretic Computational Complexity , 1974 .

[32]  G. Harman,et al.  Moral Particularism and Transduction , 2005 .

[33]  Leslie G. Valiant,et al.  A theory of the learnable , 1984, STOC '84.

[34]  Bernhard Schölkopf,et al.  KDD Cup 2001 data analysis: prediction of molecular bioactivity for drug Design-Binding to Thrombin , 2001, Knowledge Discovery and Data Mining.

[35]  Eric Gaussier,et al.  Generative vs Discriminative Approaches to Entity Recognition from Label-Deficient Data , 2004 .

[36]  Catherine Z. Elgin.  Nelson Goodman's new riddle of induction , 1997 .

[37]  Jorma Rissanen,et al.  The Minimum Description Length Principle in Coding and Modeling , 1998, IEEE Trans. Inf. Theory.

[38]  Stephen P. Stich,et al.  Justification and the Psychology of Human Reasoning , 1980, Philosophy of Science.

[39]  Manuel Blum,et al.  Toward a Mathematical Theory of Inductive Inference , 1975, Inf. Control..

[40]  D. Redelmeier,et al.  Medical decision making in situations that offer multiple alternatives. , 1995, JAMA.

[41]  K. Holyoak,et al.  Structural Dynamics of Cognition: From Consistency Theories to Constraint Satisfaction , 2002 .

[42]  K. Holyoak,et al.  Bidirectional reasoning in decision making by constraint satisfaction , 1999 .

[43]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[44]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[45]  P. Thagard,et al.  Explanatory coherence , 1993 .

[46]  G. A. Barnard,et al.  THE LOGIC OF STATISTICAL INFERENCE1 , 1972, The British Journal for the Philosophy of Science.

[47]  Sanjeev R. Kulkarni,et al.  Learning Pattern Classification - A Survey , 1998, IEEE Trans. Inf. Theory.

[48]  P. Thagard,et al.  Computational Philosophy of Science , 1988 .

[49]  A. Tversky,et al.  Judgment under Uncertainty , 1982 .

[50]  P. Thagard,et al.  Coherence in Thought and Action , 2000 .

[51]  B. Schwartz.  The Paradox of Choice: Why More Is Less , 2004 .

[52]  D. Ruppert.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .

[53]  K. Popper,et al.  Logik der Forschung , 1935 .

[54]  D. Stalker,et al.  Grue the New Riddle of Induction , 1994 .

[55]  Thorsten Joachims,et al.  Transductive Inference for Text Classification using Support Vector Machines , 1999, ICML.