Input space versus feature space in kernel-based methods.
about
Image fusion for dynamic contrast enhanced magnetic resonance imagingCharacterization of digital medical images utilizing support vector machinesA predictive framework for integrating disparate genomic data types using sample-specific gene set enrichment analysis and multi-task learningKernel-based distance metric learning for microarray data classification.Fast support vector machines for continuous data.A novel similarity-measure for the analysis of genetic data in complex phenotypes.Machine learning of accurate energy-conserving molecular force fields.Reproducible segmentation of white matter hyperintensities using a new statistical definitionPredicting decisions in human social interactions using real-time fMRI and pattern classification.Kernel and nonlinear canonical correlation analysis.Comparison of the data classification approaches to diagnose spinal cord injuryNoFold: RNA structure clustering without folding or alignmentA scatter-based prototype framework and multi-class extension of support vector machines.Patient classification as an outlier detection problem: an application of the One-Class Support Vector MachineSupport vector machine classification and characterization of age-related reorganization of functional brain networksAutomated analysis of confocal laser endomicroscopy images to detect head and neck cancer.One-Class Support Vector Machines Identify the Language and Default Mode Regions As Common Patterns of Structural Alterations in Young Children with Autism Spectrum Disorders.Reduced multiple empirical kernel learning machine.Complete fold annotation of the human proteome using a novel structural feature space.Bypassing the Kohn-Sham equations with machine learning.Arbitrary norm support vector machines.The kernel semi-least squares method for sparse distance approximation.Diagnosis of dental deformities in cephalometry images using support vector machine.Deep Restricted Kernel Machines Using Conjugate Feature Duality.SVDD-based pattern denoising.In Silico Approach for Prediction of Antifungal Peptides.Regularized Pre-image Estimation for Kernel PCA De-noisingSparse On-Line Gaussian ProcessesUnderstanding machine-learned density functionalsNonlinear gradient denoising: Finding accurate extrema from inaccurate functional derivativesAdvances in credit scoring: combining performance and interpretation in kernel discriminant analysisStorages Are Not ForeverAccelerating the kernel-method-based feature extraction procedure from the viewpoint of numerical approximationEVALUATE DISSIMILARITY OF SAMPLES IN FEATURE SPACE FOR IMPROVING KPCAMultiview cluster ensembles for multimodal MRI segmentationA Study on Multi-Scale Kernel Optimisation via Centered Kernel-Target AlignmentChanges in the seasonality of tornado and favorable genesis conditions in the central United StatesFeature selection of gene expression data for Cancer classification using double RBF-kernelsA Novel Neuron in Kernel DomainOptimizing Kernel PCA Using Sparse Representation-Based Classifier for MSTAR SAR Image Target Recognition
P2860
Q24795773-CECA6B55-535E-4FE7-9C9C-C40979CE4D20Q24806334-095EC65C-1897-4461-8EC3-C8B772832919Q30571667-BCB58CAB-0556-4A42-A973-C907EDDB854BQ31044239-9191C87B-C68D-441F-85B6-9A21311ED6ADQ33425095-889B9990-72EF-43B9-A228-EC65AEDA61D2Q33469887-66FE820F-D97B-4679-8EEC-8B933E4418C4Q33643510-7B59BDC9-74E0-48A4-9587-8D0CF515D89FQ33715348-53BF29B9-0575-4E8D-AFAF-B516B5748864Q34049256-82898B31-A9B2-4D34-9228-923480CC9103Q34150168-B9260EF2-2DA2-46CA-95F3-10DFC59E821AQ34219696-99540AFB-C0A2-492E-A120-13722039559BQ34363855-5C422BAB-4CCE-424E-9ADB-AD88AF32755FQ34464358-0AD61668-6DD4-4BF0-AAF0-A5E3F1F87393Q35317564-32EAFAD5-B80A-4449-A27F-1BB0D33143D9Q35784018-9DD4A11D-0C4C-47FB-A12B-E665D788F137Q35839102-D4528B10-5A33-4D8B-9E0B-E5DFECFF093BQ37048741-25E5CADF-B92E-49B5-A45B-34DA6C59FDFEQ40860535-09B2CAF1-A6DD-41EA-9B07-3BE166627550Q42155732-E9BAECF7-CC33-4574-BA29-25283DED35D6Q42261808-8241D385-D2D0-4385-83AB-99EBE54C905EQ44346351-3CF2B304-8DF0-453E-95C2-BD0FA036F719Q44843185-8C7CF41D-1777-4CEB-8483-9483D0A7BD91Q47419670-6D1AE8C2-DFF5-4FD8-BF51-8AE0B96EF54FQ50436163-F05A5B99-76EF-46B8-83B4-2FF7817C2947Q50996971-CAA1C8B3-1EB4-47F9-B37F-8F312FE98043Q52657164-968C0446-FAD3-4EB0-A118-2FE371BBEE89Q53557114-520B6511-F2CF-48EE-A34C-8B14147F9BC3Q56214492-E587D5A3-98AF-4D4E-85C3-BFB3515770B4Q56919353-3281A465-91A2-46C4-A7D2-AF316C888D91Q56919383-705D8BC9-AEDE-470E-B89D-8CFA4EFE328DQ57599357-BBAAFE10-1C4D-49F7-8667-2B5C4CE1037DQ57727527-2D0D5D83-ED35-4E22-83CE-718FA44EB7B3Q57809726-B38328E9-EE95-47BA-B721-DE1B9CC84315Q57809799-EF884268-1C3A-457B-A70F-24B2E0D1F64EQ57830411-91FB3940-1FF2-4355-8FA3-9B0E4C7E6D58Q58046526-C48C90C6-75B6-4827-A908-D92C848482DAQ58091503-5A033006-6D83-44D0-9A8F-BD5AD24D9F8DQ58101114-0C5CB980-D000-47D3-918C-7F06583D365DQ58998431-5A10154C-E4EE-4190-AEC9-6E54ADBB5292Q59030629-35377E23-2DC5-4142-A9A7-767EAE367EFB
P2860
Input space versus feature space in kernel-based methods.
description
article científic
@ca
article scientifique
@fr
articolo scientifico
@it
artigo científico
@pt
bilimsel makale
@tr
scientific article published on January 1999
@en
vedecký článok
@sk
vetenskaplig artikel
@sv
videnskabelig artikel
@da
vědecký článek
@cs
name
Input space versus feature space in kernel-based methods.
@en
Input space versus feature space in kernel-based methods.
@nl
type
label
Input space versus feature space in kernel-based methods.
@en
Input space versus feature space in kernel-based methods.
@nl
prefLabel
Input space versus feature space in kernel-based methods.
@en
Input space versus feature space in kernel-based methods.
@nl
P2093
P356
P1476
Input space versus feature space in kernel-based methods.
@en
P2093
P304
P356
10.1109/72.788641
P577
1999-01-01T00:00:00Z