advanced supervised learning frameworks
Alan S. Lapedes
algorithms
Bayesian inference methods
BCH Code
Cart Tree
Category=PB
Chong Gu
Christopher C. J. Burges
complexity science applications
Computational Learning Theory
concept
David H. Wolpert
David Haussler
Ecc
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
error
examples
exhaustive
Exhaustive Learning
Generalization Error
Geoffrey E. Hinton
Ghulum Bakiri
Grace Wahba
Hidden Units
Hinge Functions
Hussein Almuallim
Hypothesis Functions
Hypothesis Space
image recognition algorithms
Instance Space
John S. Denker
learning
Learning Algorithms
Leo Breiman
Manfred Warmuth
Minimum Pairwise Distance
MML.
Model III
Naftali Tishby
neural network optimization
Noisy Patterns
Penalty Functional
Peter Cheeseman
PSA Model
Richard Chappell
risk estimation techniques
set
statistical learning theory
Steven J. Nowlan
Steven Nowlan
supervised
Supervised Learning
target
Target Concept
Thomas G. Dietterich
training
Training Error
Training Set
Training Set Size
UCI Repository
Worst Case Probability
Yuedong Wang
Product details
- ISBN 9780367320515
- Weight: 453g
- Dimensions: 152 x 229mm
- Publication Date: 07 May 2019
- Publisher: Taylor & Francis Ltd
- Publication City/Country: GB
- Product Form: Hardback
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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.
David. H Wolpert
