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An Improved Boosting Algorithm for Machine Learning and Pattern Recognition

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

技术应用: -- 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

详细技术说明: 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...

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

*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.

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