Support Vector Machines as Probabilistic Models. | 12 | 0.83 | 2011 |
A New Scatter-Based Multi-Class Support Vector Machine | 0 | 0.34 | 2011 |
Non-Sparse Regularization and Efficient Training with Multiple Kernels | 17 | 1.03 | 2010 |
The SHOGUN Machine Learning Toolbox | 116 | 19.53 | 2010 |
mGene.web: a web service for accurate computational gene finding. | 4 | 0.78 | 2009 |
Efficient and Accurate Lp-Norm Multiple Kernel Learning. | 47 | 1.54 | 2009 |
The Feature Importance Ranking Measure | 8 | 0.80 | 2009 |
An Automated Combination of Kernels for Predicting Protein Subcellular Localization | 14 | 0.65 | 2008 |
POIMs: positional oligomer importance matrices--understanding support vector machine-based signal detectors. | 23 | 1.74 | 2008 |
Multiclass multiple kernel learning | 113 | 5.57 | 2007 |
Transductive support vector machines for structured variables | 27 | 1.37 | 2007 |
A continuation method for semi-supervised SVMs | 63 | 3.17 | 2006 |
ARTS: accurate recognition of transcription starts in human. | 50 | 5.65 | 2006 |
Semi-Supervised Classification by Low Density Separation. | 304 | 16.91 | 2005 |
Large margin non-linear embedding | 5 | 0.88 | 2005 |
Learning to Find Graph Pre-images | 13 | 0.68 | 2004 |
Prediction on Spike Data Using Kernel Algorithms | 11 | 1.11 | 2003 |
Microarrays: how many do you need? | 10 | 7.60 | 2003 |
Co-clustering of biological networks and gene expression data. | 143 | 8.43 | 2002 |
Confidence measures for protein fold recognition. | 7 | 1.09 | 2002 |
Confidence Measures for Fold Recognition | 1 | 0.47 | 2001 |
Centralization: a new method for the normalization of gene expression data. | 25 | 10.71 | 2001 |
A simple iterative approach to parameter optimization. | 10 | 1.07 | 2000 |
Engineering support vector machine kernels that recognize translation initiation sites. | 177 | 29.62 | 2000 |
Analysis of gene expression data with pathway scores. | 53 | 24.82 | 2000 |
Application of parameter optimization to molecular comparison problems. | 2 | 0.55 | 1999 |