April-May 2014    Workshop

" Kernel Based Automatic Learning " 

a series of 16  lectures  (open access) organized on the UH Campus  by

Robert Azencott 

with the collaboration of a group of UH graduate students   

             Audrey Cheong, Tasadduk Chowdury, Nandini Deka,  Kedar Grama, Aixia Guo, Zhuo Liu, Murad Megjhani, Behrang Mehrparvar, Viktoria Muravina, Nikolaos Mitsakos, Burcin Ozcan, Erte Pan, Danil Safin, James Winkle, Yan Xu, Shihay Zhao

These lectures were integrated into   R. Azencott's  UH graduate course on
" Data Mining and Kernel based Automatic Learning " 

Lecture slides are downloadable below and cover the following topics

" SVM Learning : Multi-Class Discrimination " by Viktoria Muravina   

" SVM Learning :  Kernel Parameters Selection "by Nandini Deka 

" Online Learning with Multiple Kernels  " by Audrey Cheong 

" Deep Learning : Kernel Analysis of Networks Layers " by Behrang Mehrparvar 

Kernel Clustering

" Support Vectors Clustering " by Zhuo Liu

" Kernel Methods for Clustering " by Kedar Grama

" Kernel based Clustering " by Erte Pan

Kernel Regression

" Kernel Regression : Travel Time Prediction " by Danil Safin

" Kernel Regression : Time Series " by Nikolaos  Mitsakos

Artificial Vision applications

" Object Recognition : Kernel Based Dictionaries " by Murad Megjhani 

" Object Recognition : SVM Learning  " by Tasadduk Chowdury 

" Robot Vision :  Kernelized Bayes Rule " by Yan Xu

Genomics and Proteomic applications

" Gene Functions : Kernel Based Identification  " by James Winkle

" Phylogenetic Profiles Classification :  Tree Kernels  " by Burcin Ozcan

" Protein Sequences Classification :  HMM based Kernels " by Shihay Zhao  

" Microarray Data   : Key Genes Selection by SVM " by Aixia Guo