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A generalized divergence measure for nonnegative matrix factorizationMixed signal learning by spike correlation propagation in feedback inhibitory circuits.Data analysis techniques in phosphoproteomics.Feature extraction through LOCOCODE.Head-centric disparity and epipolar geometry estimation from a population of binocular energy neurons.A taxonomy for spatiotemporal connectionist networks revisited: the unsupervised case.Mirrored STDP Implements Autoencoder Learning in a Network of Spiking Neurons.Biohybrid Control of General Linear Systems Using the Adaptive Filter Model of Cerebellum.Nonlinear complex-valued extensions of Hebbian learning: an essay.The divergent autoencoder (DIVA) model of category learning.Beta Hebbian Learning as a New Method for Exploratory Projection Pursuit.Active inference and epistemic value.Introducing asymmetry into interneuron learning.Unsupervised neural learning on lie group.Incremental slow feature analysis: adaptive low-complexity slow feature updating from high-dimensional input streams.A new modulated Hebbian learning rule--biologically plausible method for local computation of a principal subspace.A geometric Newton method for Oja's vector field.Stiefel-manifold learning by improved rigid-body theory applied to ICA.Preintegration lateral inhibition enhances unsupervised learning.Dynamic model of visual recognition predicts neural response properties in the visual cortex.Time-oriented hierarchical method for computation of principal components using subspace learning algorithm.Why Do Similarity Matching Objectives Lead to Hebbian/Anti-Hebbian Networks?Automatic classification of transiently evoked otoacoustic emissions using an artificial neural network.Singular value decomposition learning on double Stiefel manifold.Adaptive synchronization of activities in a recurrent network.Dimensional reduction for reward-based learning.A robust subspace algorithm for principal component analysis.Attractor networks for shape recognition.Separating style and content with bilinear models.Factor analysis using delta-rule wake-sleep learning.An alternative perspective on adaptive independent component analysis algorithmsA First Application of Independent Component Analysis to Extracting Structure from Stock ReturnsTime-dependent changes in effective connectivity measured with PETLearning Factorial Codes by Predictability MinimizationSIMULTANEOUS DETERMINATION OF Os(VIII) AND Ru(IV) AS CATALYSTS THROUGH A SINGLE CATALYTIC KINETIC RUN USING PRINCIPAL COMPONENT ARTIFICIAL NEURAL NETWORKJust Imagine! Learning to Emulate and Infer Actions with a Stochastic Generative ArchitecturePower Cell SOC Modelling for Intelligent Virtual Sensor Implementation
P2860
Q28288067-E434DB2E-AC32-4F66-A6F9-1D2DEE069157Q30412252-B18A756D-EC20-4B43-A2AC-A4A8DD9F32FBQ30860024-8F62A0E1-0A82-41C4-AB87-9A0A752BCDF6Q33543278-823FE6DD-1D4E-448F-9C53-D36DD44D0D82Q34694070-E9214114-6401-4FC7-B4FD-FB6BB45C2F57Q35158305-BC95F549-4326-43DE-BD4D-BCED4E039720Q35860126-FFF5A685-D157-4BFF-B8F8-DD52D848F2A3Q35866332-D0CE78B6-0E20-4D7B-9F9A-196800BDC561Q36097315-D653DCB6-112E-4098-A368-308505ADACEAQ38394100-4BEA140E-549E-42C1-B082-CE4BCCB06C0CQ38831726-67A73706-B9EB-4DE5-982D-C97BB4A1F6DEQ39038045-8714F312-7658-4FF3-AAFA-3A34FC7D6B6EQ40429793-731AB820-16E2-440F-AC67-902A83C396CBQ44172609-0DECA458-63B1-4101-A5B3-311369456F76Q44376792-8C1991ED-1F7C-42AF-A170-61445CACCA73Q44578629-8759B641-BEFA-4FCF-97B5-A843E0F80E97Q46238872-DE21FF7A-29C9-4F8B-8DBA-37A72390BB95Q47345631-A2EF9623-2204-4C0D-B87E-1D7B233EE3FCQ48507811-0852843C-D7CF-4971-94E3-C4ED6F951A10Q48706602-CA755F3C-F020-4F9E-B12D-3F22C8577E67Q48957154-CEAA23F6-C3FA-4E64-9E4A-AFE0C445578DQ49683058-B139DFA9-2AAC-4ACC-A59E-2F2E21D90ACCQ50502491-992A6BAE-78CE-4EF3-BCF6-08F49EEAC056Q51638649-5974A9C0-9780-4040-9643-CAD56268F8F8Q51853024-6A737BC6-BE88-49C7-9834-9C1D6A5A2DDCQ51926872-D1C02DB6-DCFC-4771-82C3-89834B5D0A8DQ52007096-AD337E7B-1E24-43B4-83E4-1B861691A2D7Q52019499-7A716A39-5620-4BEB-9A4A-E953E48F8976Q52075536-1033D0A4-2C34-4C20-8775-F49B132AB2E7Q52192469-BC59360E-DC90-4C9F-8250-4610A8D038B8Q52231960-E8E2CB30-557F-4516-BDB0-3E76B3CDA279Q56341927-E7409F85-2AF6-4549-89D8-4391CC2DF003Q57004472-50175766-2153-41D8-A2D8-E962B5381266Q57308324-CBA29BB7-C60D-4439-99B7-318F73E2C744Q58272221-802BEFE8-4214-428B-BA51-C3883D341EDBQ58643658-3B06F549-186F-4B88-B371-9CA6EEACED4AQ59194574-6DFA34EC-DD01-4E51-8E78-4B2E375DDAA7
P2860
description
im Jahr 1989 veröffentlichter wissenschaftlicher Artikel
@de
wetenschappelijk artikel
@nl
наукова стаття, опублікована в січні 1989
@uk
name
NEURAL NETWORKS, PRINCIPAL COMPONENTS, AND SUBSPACES
@en
NEURAL NETWORKS, PRINCIPAL COMPONENTS, AND SUBSPACES
@nl
type
label
NEURAL NETWORKS, PRINCIPAL COMPONENTS, AND SUBSPACES
@en
NEURAL NETWORKS, PRINCIPAL COMPONENTS, AND SUBSPACES
@nl
prefLabel
NEURAL NETWORKS, PRINCIPAL COMPONENTS, AND SUBSPACES
@en
NEURAL NETWORKS, PRINCIPAL COMPONENTS, AND SUBSPACES
@nl
P921
P1476
NEURAL NETWORKS, PRINCIPAL COMPONENTS, AND SUBSPACES
@en
P356
10.1142/S0129065789000475
P50
P577
1989-01-01T00:00:00Z