Sparse matrices in matlab: design and implementation
SIAM Journal on Matrix Analysis and Applications
Machine Learning
Empirical bayes screening for multi-item associations
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Proximal support vector machine classifiers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining with sparse grids using simplicial basis functions
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
SSVM: A Smooth Support Vector Machine for Classification
Computational Optimization and Applications
Classes of kernels for machine learning: a statistics perspective
The Journal of Machine Learning Research
Classifying large data sets using SVMs with hierarchical clusters
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Making SVMs Scalable to Large Data Sets using Hierarchical Cluster Indexing
Data Mining and Knowledge Discovery
Optimising Kernel Parameters and Regularisation Coefficients for Non-linear Discriminant Analysis
The Journal of Machine Learning Research
Feature selection methods for text classification
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A new intrusion detection system using support vector machines and hierarchical clustering
The VLDB Journal — The International Journal on Very Large Data Bases
Hierarchical clustering support vector machines for classifying type-2 diabetes patients
ISBRA'08 Proceedings of the 4th international conference on Bioinformatics research and applications
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part II
A hybrid method for speeding SVM training
NGITS'06 Proceedings of the 6th international conference on Next Generation Information Technologies and Systems
A New SVM Reduction Strategy of Large-Scale Training Sample Sets
International Journal of Advanced Pervasive and Ubiquitous Computing
| Hi-index | 0.00 |
We give a statistical interpretation of Proximal Support Vector Machines (PSVM) proposed at KDD2001 as linear approximaters to (nonlinear) Support Vector Machines (SVM). We prove that PSVM using a linear kernel is identical to ridge regression, a biased-regression method known in the statistical community for more than thirty years. Techniques from the statistical literature to estimate the tuning constant that appears in the SVM and PSVM framework are discussed. Better shrinkage strategies that incorporate more than one tuning constant are suggested. For nonlinear kernels, the minimization problem posed in the PSVM framework is equivalent to finding the posterior mode of a Bayesian model defined through a Gaussian process on the predictor space. Apart from providing new insights, these interpretations help us attach an estimate of uncertainty to our predictions and enable us to build richer classes of models. In particular, we propose a new algorithm called PSVMMIX which is a combination of ridge regression and a Gaussian process model. Extension to the case of continuous response is straightforward and illustrated with example datasets.