Gal A. Kaminka's Publications

Sorted by DateClassified by Publication TypeClassified by TopicGrouped by Student (current)Grouped by Former Students

Obtaining Scalable and Accurate Classification in Large Scale Spatiotemporal Domains

Igor Vainer. Obtaining Scalable and Accurate Classification in Large Scale Spatiotemporal Domains. Master's Thesis, Bar Ilan University,2009.

Download

[PDF]3.9MB  

Abstract

We present an approach for learning models that obtain accurate classification of data objects, collected in large scale spatiotemporal domains. The model generation is structured in three phases: spatial dimension reduction, spatiotemporal features extraction, and feature selection. Novel techniques for the first two phases are presented, with two alternatives for the middle phase. We explore model generation based on the combinations of techniques from each phase. We apply the introduced methodology to datasets from the Voltage-Sensitive Dye Imaging (VSDI) domain, where the resulting classification models successfully decode neuronal population responses in the visual cortex of behaving animals. VSDI is currently the best technique enabling simultaneous high spatial ( points) and temporal ( or less) resolution imaging from neuronal population in the cortex. We demonstrate that not only our approach is scalable enough to handle computationally challenging data, but it also contributes to the neuroimaging field of study with its decoding abilities. The effectiveness of our methodology is further explored on a dataset from the hurricanes domain, and a promising direction, based on the preliminary results of hurricane severity classification, is revealed.

Additional Information

BibTeX

@MastersThesis{igor-msc,
author = {Igor Vainer},
title = {Obtaining Scalable and Accurate Classification in Large Scale Spatiotemporal Domains},
school = {{B}ar {I}lan {U}niversity},
year = {2009},
OPTkey = {},
OPTtype = {},
OPTaddress = {},
OPTmonth = {},
OPTnote = {},
OPTannote = {},
  wwwnote = {}, 
  abstract = { We present an approach for learning models that obtain accurate classification of data  objects,  collected in large scale spatiotemporal domains. The model generation is structured in three phases:  spatial  dimension reduction, spatiotemporal features extraction, and feature selection. Novel techniques for  the first  two phases are presented, with two alternatives for the middle phase. We explore model generation  based on  the combinations of techniques from each phase. We apply the introduced methodology to datasets from  the  Voltage-Sensitive Dye Imaging (VSDI) domain, where the resulting classification models successfully decode  neuronal population responses in the visual cortex of behaving animals. VSDI is currently the best technique  enabling simultaneous high spatial ( points) and temporal ( or less) resolution imaging from neuronal  population in the cortex. We demonstrate that not only our approach is scalable enough to handle  computationally challenging data, but it also contributes to the neuroimaging field of study with its decoding  abilities. The effectiveness of our methodology is further explored on a dataset from the hurricanes domain,  and a promising direction, based on the preliminary results of hurricane severity classification, is revealed. } 
}

Generated by bib2html.pl (written by Patrick Riley ) on Sun Oct 29, 2017 21:31:22