about
Learning from noisy data: An exactly solvable model.Neural networks and perceptual learning.Receiver operating characteristics of perceptrons: influence of sample size and prevalence.Social interaction as a heuristic for combinatorial optimization problems.Implications of neuronal diversity on population coding.Optimal pruning in neural networks.Expectation propagation with factorizing distributions: a Gaussian approximation and performance results for simple models.Adaptive and self-averaging Thouless-Anderson-Palmer mean-field theory for probabilistic modeling.Inference by replication in densely connected systems.Concatenated retrieval of correlated stored information in neural networks.Learning by message passing in networks of discrete synapses.Asymptotic properties of the Fisher kernel.Information space dynamics for neural networks.On-line learning of unrealizable tasks.Cavity approach to noisy learning in nonlinear perceptrons.Statistical mechanics of learning with soft margin classifiers.Retarded learning: rigorous results from statistical mechanics.Variational studies and replica symmetry breaking in the generalization problem of the binary perceptronThe VC dimension for mixtures of binary classifiers.Generalization and capacity of extensively large two-layered perceptrons.Reparametrization-covariant theory for on-line learning of probability distributions.Information theory approach to learning of the perceptron rule.Algorithmic stability and sanity-check bounds for leave-one-out cross-validation.On-line versus Off-line Learning from Random Examples: General Results.On-line Gibbs learning.Learning by examples from a nonuniform distribution.Perturbative treatments and learning techniques.Unsupervised learning by examples: On-line versus off-line.Mean field approach to Bayes learning in feed-forward neural networks.Bounds for predictive errors in the statistical mechanics of supervised learning.The target switch algorithm: a constructive learning procedure for feed-forward neural networks.Learning and generalization with Minimerror, a temperature-dependent learning algorithm.On-line learning in soft committee machines.On-line versus off-line learning in the linear perceptron: A comparative study.Local and global convergence of on-line learning.Gradient descent learning in perceptrons: A review of its possibilities.Exact solution for on-line learning in multilayer neural networks.Learning to classify in large committee machines.Learning and generalization in a two-layer neural network: The role of the Vapnik-Chervonvenkis dimension.Scaling laws in learning of classification tasks.
P2860
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P2860
description
1992 nî lūn-bûn
@nan
1992年の論文
@ja
1992年学术文章
@wuu
1992年学术文章
@zh
1992年学术文章
@zh-cn
1992年学术文章
@zh-hans
1992年学术文章
@zh-my
1992年学术文章
@zh-sg
1992年學術文章
@yue
1992年學術文章
@zh-hant
name
Statistical mechanics of learning from examples.
@en
Statistical mechanics of learning from examples.
@nl
type
label
Statistical mechanics of learning from examples.
@en
Statistical mechanics of learning from examples.
@nl
prefLabel
Statistical mechanics of learning from examples.
@en
Statistical mechanics of learning from examples.
@nl
P2093
P2860
P356
P1433
P1476
Statistical mechanics of learning from examples.
@en
P2093
P2860
P304
P356
10.1103/PHYSREVA.45.6056
P407
P577
1992-04-01T00:00:00Z