Media.Player

An Improved Boosting Algorithm for Machine Learning and Pattern Recognition

Technology Benefits

-- Better classification accuracy for any boosting technology-- May be readily integrated into current machine learning algorithms

Technology Application

-- Image processing and recognition-- MRI automated diagnosis; e.g. analyzing scans for irregularities such as tumors, fractures, blood clots-- Computer vision; automated driving, identifying obstacles, avoiding collisions-- Security technologies; identifying explosive material chemical signatures from spectroscopy data, identifying weapons on mm-Wave images-- Character and handwriting recognition-- Speech recognition-- Systems biology and drug discovery

Detailed Technology Description

A boosting algorithm for machine learning and pattern recognition that improves on existing algorithms (e.g., Adaboost) by attempting to minimize both the error and the variance in classification problemsIn machine learning, classificatio...

*Abstract

None

*Inquiry

Calvin ChuColumbia Technology VenturesTel: (212) 854-8444Email: TechTransfer@columbia.edu

*IR

CU12127

*Principal Investigator

*Publications

-- Shivaswamy, P.K. and T. Jebara. "Empirical Bernstein Boosting." Thirteenth International Conference on Artificial Intelligence and Statistics, AISTATs, May 2010. -- Shivaswamy P.K. and T. Jebara. "Variance Penalizing AdaBoost." Neural Information Processing Systems (NIPS), December 2011.

Country/Region

USA