This book provides different mathematical frameworks for addressing supervised learning. It is based on a workshop held under the auspices of the Center for Nonlinear Studies at Los Alamos and the Santa Fe Institute in the summer of 1992.
This book provides different mathematical frameworks for addressing supervised learning. It is based on a workshop held under the auspices of the Center for Nonlinear Studies at Los Alamos and the Santa Fe Institute in the summer of 1992.
About the Santa Fe Institute Santa Fe Institute Studies in the Sciences of Complexity Preface The Status of Supervised Learning Science Circa 1994: The Search for a Consensus Reflections After Refereeing Papers for NIPS The Probably Approximately Correct (PAC) and Other Learning Models Decision Theoretic Generalizations of the PAC Model for Neural Net and Other Learning Applications The Relationship Between PAC, the Statistical Physics Framework, the Bayesian Framework, and the VC Framework Statistical Physics Models of Supervised Learning On Exhaustive Learning A Study of Maximal Coverage Learning Algorithms On Bayesian Model Selection Soft Classification, a.k.a. Risk Estimation, via Penalized Log Likelihood and Smoothing Spline Analysis of Variance Current Research Preface to "Simplifying Neural Networks by Soft Weight Sharing" Simplifying Neural Networks by Soft Weight Sharing Error Correcting Output Codes: A General Method for Improving Multiclass Inductive Learning Programs Image Segmentation and Recognition
About the Santa Fe Institute Santa Fe Institute Studies in the Sciences of Complexity Preface The Status of Supervised Learning Science Circa 1994: The Search for a Consensus Reflections After Refereeing Papers for NIPS The Probably Approximately Correct (PAC) and Other Learning Models Decision Theoretic Generalizations of the PAC Model for Neural Net and Other Learning Applications The Relationship Between PAC, the Statistical Physics Framework, the Bayesian Framework, and the VC Framework Statistical Physics Models of Supervised Learning On Exhaustive Learning A Study of Maximal Coverage Learning Algorithms On Bayesian Model Selection Soft Classification, a.k.a. Risk Estimation, via Penalized Log Likelihood and Smoothing Spline Analysis of Variance Current Research Preface to "Simplifying Neural Networks by Soft Weight Sharing" Simplifying Neural Networks by Soft Weight Sharing Error Correcting Output Codes: A General Method for Improving Multiclass Inductive Learning Programs Image Segmentation and Recognition
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