Vineeth N Balasubramanian from Gilbert, AZ, age 46

Vineeth Balasubramanian Phones & Addresses

Gilbert, AZ
Tempe, AZ

Work

Company:

Arizona state university
2005
Position:

Teaching associate

Education

School / High School:

Arizona State University
2005 to 2009

Skills

Research: Machine Learning • Pattern Recognition • Information Fusion • Computer Vision • Applications: Assistive Technology • Clinical Decision Support • Biometrics

Industries

Research

Resumes

Research Associate/Doctoral Student At Arizona State University

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Position:

Teaching Associate at Arizona State University, Research Associate at Center for Cognitive Ubiquitous Computing (CUbiC), Arizona State University

Location:

Phoenix, Arizona Area

Industry:

Research

Work:

Arizona State University since 2005
Teaching Associate
Center for Cognitive Ubiquitous Computing (CUbiC), Arizona State University since May 2005
Research Associate
Oracle Oct 2003 - Jul 2005
Applications Engineer

Education:

Arizona State University 2005 - 2009
Sri Sathya Sai Institute of Higher Learning 2001 - 2003
M.Tech, Computer Science

Skills:

Research: Machine Learning
Pattern Recognition
Information Fusion
Computer Vision
Applications: Assistive Technology
Clinical Decision Support
Biometrics

Us Patents

Adaptive Batch Mode Active Learning For Evolving A Classifier
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US Patent:

20120310864, Dec 6, 2012
Filed:

May 31, 2012
Appl. No.:

13/484696
Inventors:

Shayok Chakraborty - Tempe AZ, US
Vineeth Nallure Balasubramanian - Tempe AZ, US
Sethuraman Panchanathan - Gilbert AZ, US
International Classification:

G06F 15/18
US Classification:

706 12
Abstract:

This disclosure includes various embodiments of apparatuses, systems, and methods for adaptive batch mode active learning for evolving a classifier. A corpus of unlabeled data elements to be classified is received, a batch size is determined based on a score function, a batch of unlabeled data elements having the determined batch size is selected from the corpus and labeled using a labeling agent or oracle, a classifier is retrained with the labeled data elements, these steps are repeated until a stop criterion has been met, for example, the classifier obtains a desired performance on unlabeled data elements in the corpus. The batch size determination and selection of a batch unlabeled data elements may be based on a single score function. The data elements may be video, image, audio, web text, and/or other data elements.

Vineeth N Balasubramanian