Unsupervised Learning: Foundations of Neural ComputationGeoffrey Hinton, Terrence J. Sejnowski MIT Press, 1999. gada 24. maijs - 418 lappuses Since its founding in 1989 by Terrence Sejnowski, Neural Computation has become the leading journal in the field. Foundations of Neural Computation collects, by topic, the most significant papers that have appeared in the journal over the past nine years. This volume of Foundations of Neural Computation, on unsupervised learning algorithms, focuses on neural network learning algorithms that do not require an explicit teacher. The goal of unsupervised learning is to extract an efficient internal representation of the statistical structure implicit in the inputs. These algorithms provide insights into the development of the cerebral cortex and implicit learning in humans. They are also of interest to engineers working in areas such as computer vision and speech recognition who seek efficient representations of raw input data. |
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1.–5. rezultāts no 70.
viii. lappuse
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Saturs
Local Synaptic Learning Rules Suffice to Maximize Mutual Information | 19 |
Emergence of PositionIndependent Detectors of Sense of Rotation | 47 |
Learning Invariance from Transformation Sequences | 63 |
What Is the Goal of Sensory Coding? | 101 |
An InformationMaximization Approach to Blind Separation and Blind | 145 |
Natural Gradient Works Efficiently in Learning | 177 |
A Fast FixedPoint Algorithm for Independent Component Analysis | 203 |
Learning Mixture Models of Spatial Coherence | 223 |
Finding Minimum Entropy Codes | 249 |
Factor Analysis Using DeltaRule WakeSleep Learning | 293 |
Dimension Reduction by Local Principal Component Analysis | 317 |
A ResourceAllocating Network for Function Interpolation | 341 |
Clustering with Point and Graph | 355 |
Learning to Generalize from Single Examples in the Dynamic Link | 373 |
391 | |
Citi izdevumi - Skatīt visu
Unsupervised Learning: Foundations of Neural Computation Geoffrey Hinton,Terrence J. Sejnowski Priekšskatījums nav pieejams - 1999 |
Unsupervised Learning: Foundations of Neural Computation Geoffrey Hinton,Terrence J. Sejnowski Priekšskatījums nav pieejams - 1999 |
Unsupervised Learning: Foundations of Neural Computation Geoffrey E. Hinton,Terrence Joseph Sejnowski Priekšskatījums nav pieejams - 1999 |
Bieži izmantoti vārdi un frāzes
activity Amari amplitude anti-Hebbian backpropagation Barlow Becker and Hinton blind deconvolution blind separation blind source separation cells component analysis Computation connections constraint convergence correlations decorrelation derived described disparity distribution entropy equation estimate example feature feature extraction filter function gaussian Hebbian Hebbian learning hidden units histograms implicit space independent component analysis independent components input invariant iterations kurtosis layer learning algorithm learning rate learning rule linear Linsker maps matrix maximizing maximum likelihood method minimizing MST-like unit MT-like mutual information natural gradient natural scenes Neural Comp neural network neurons noise nonlinear optimal output unit parameters patterns perceptual phase pixels principal component analysis principal components probability problem processing random receptive fields redundancy representation response rotation Sejnowski sensory sigmoid signal solution sparse code statistical stereo stochastic synaptic theory tion transform unsupervised learning update values variables variance visual cortex visual system wake-sleep wavelet weight vector
Atsauces uz šo grāmatu
Face Image Analysis by Unsupervised Learning Marian Stewart Bartlett Ierobežota priekšskatīšana - 2001 |
Self-organizing Map Formation: Foundations of Neural Computation Klaus Obermayer,Terrence Joseph Sejnowski Ierobežota priekšskatīšana - 2001 |