GEOMETRIC PROGRAMMING FOR AGGREGATION OF BINARY CLASSIFIERS
Machine Learning Methods and Applications
Přednášející: Seungjin Choi, Autoři: Sunho Park, Seungjin Choi, POSTECH, Republic of Korea
Multiclass classification problems are often decomposed into multiple binary problems that are solved by individual binary classifiers whose results are integrated into a final answer. We present a convex optimization-based method for aggregating results of binary classifiers in an optimal way to estimate class membership probabilities. We model the class membership probability as a softmax function whose input argument is a conic combination of discrepancies induced by individual binary classifiers. With this model, we formulate the l_1-regularized maximum likelihood estimation as a convex optimization that is solved by geometric programming. Numerical experiments on several UCI datasets demonstrate the high performance of our method, compared to existing methods.
Příbuzné přednášky
0:19:38
A LEARNING-BASED APPROACH TO EXPLOSIVES DETECTION USING MULTI-ENERGY X-RAY COMPUTED TOMOGRAPHY
Machine Learning Methods and Applications
Přidáno: 21. 6. 2011 20:24
0:20:06
ENTROPY ESTIMATION USING THE PRINCIPLE OF MAXIMUM ENTROPY
Machine Learning Methods and Applications
Přidáno: 21. 6. 2011 17:21
Komentáře