about
Predictive Big Data Analytics: A Study of Parkinson's Disease Using Large, Complex, Heterogeneous, Incongruent, Multi-Source and Incomplete Observations.Support vector machines for dyadic data.Breast Tumor Classification Based on a Computerized Breast Imaging Reporting and Data System Feature System.A novel approach for selecting combination clinical markers of pathology applied to a large retrospective cohort of surgically resected pancreatic cysts.Active Learning Using Hint Information.Relative density-ratio estimation for robust distribution comparison.Reasoning and Knowledge Acquisition Framework for 5G Network Analytics.A conditional entropy minimization criterion for dimensionality reduction and multiple kernel learning.A boosting approach for prediction of protein-RNA binding residues.Critical assessment and performance improvement of plant-pathogen protein-protein interaction prediction methods.A unified classification model based on robust optimization.Efficient learning and feature selection in high-dimensional regression.Regularized Multitask Learning for Multidimensional Log-Density Gradient Estimation.Validation-based sparse Gaussian process classifier design.Robust boosting algorithm against mislabeling in multiclass problems.Robust loss functions for boosting.Kernel least-squares models using updates of the pseudoinverse.Robustifying AdaBoost by adding the naive error rate.A new discriminative kernel from probabilistic models.Natural discriminant analysis using interactive Potts models.Secure access control and large scale robust representation for online multimedia event detection.BIAS-VARIANCE CONTROL VIA HARD POINTS SHAVINGBOOSTING ONE-CLASS SUPPORT VECTOR MACHINES FOR MULTI-CLASS CLASSIFICATIONAnalysis of Generalization Ability for Different AdaBoost Variants Based on Classification and Regression TreesLDA boost classification: boosting by topics
P2860
Q26779516-968D9B6A-E8B9-4694-BFE2-6E6691393720Q31043807-53181AB2-7EBC-4F60-9974-B9FC177E9850Q38626466-34624E6E-4866-4C94-A142-17A23CD18C4AQ39665396-B511F6E9-BE61-42CB-A8BE-AB510B3F5E32Q40830695-C3F07C2D-39CB-4E79-97FA-20F094CA3315Q44657040-C4955481-F7B4-46C0-B641-3D6A712938D3Q45373110-76650E24-AC58-45F5-A04D-CCD783E55188Q45410578-90153D4C-91F6-4D75-85C2-6BB55F24047EQ45943152-06D08DAB-5AA9-44A2-9CCA-C180BA398E67Q45945011-0C14750A-B8E2-40F8-A85D-1D0969332BB3Q45959832-2C28C7B8-BB06-4AC4-8C61-7F759EEB7C60Q48371649-59DCEDC1-EDD3-4F02-9245-D2686974EBFCQ50513557-C0AD4E10-B008-48E0-910A-68B28ED5669FQ51848269-F239EA8B-D13D-4F16-84D0-3BD8DF9D6F16Q51896823-59E31F1D-9A8B-4989-96EF-DE6E3CB24919Q51912448-460FFB53-96ED-4D77-BFD9-07DB286995C3Q51930657-31153F99-F382-4341-A09D-8D03AC97433FQ52001342-7409864A-EBB2-458B-8E07-0DEF1770E4F1Q52029901-BCDD1BC4-864B-4240-A584-858C53E89A3FQ52123459-777F1C5D-C7D7-4C4F-8EAE-198A02618611Q53467513-17BA1209-00D1-4537-AAAD-3AAAC07D1ADAQ57314636-DAABEE2A-325F-492E-BF84-CFF06DBDE17BQ58255016-41FB3489-B6A3-4170-BF6F-E2D36D4F71E5Q59112703-04778D6F-127B-4CD4-BD6B-CD0BF201F017Q59290590-6F6BEB0C-DD6A-4502-B57A-3DA21D904936
P2860
description
im Januar 2001 veröffentlichter wissenschaftlicher Artikel
@de
wetenschappelijk artikel
@nl
наукова стаття, опублікована у 2001
@uk
type
P356
P1433
P2093
K.-R. Müller
P2888
P304
P356
10.1023/A:1007618119488
P577
2001-01-01T00:00:00Z
P6179
1003090683