TY - GEN

T1 - Discriminative learning via semidefinite probabilistic models

AU - Crammer, Koby

AU - Globerson, Amir

PY - 2006

Y1 - 2006

N2 - Discriminative linear models are a popular tool in machine learning. These can be generally divided into two types: linear classifiers, such as support vector machines (SVMs), which are well studied and provide state-of-the-art results, and probabilistic models such as logistic regression. One shortcoming of SVMs is that their output (known as the "margin") is not calibrated, so that it is difficult to incorporate such models as components of larger systems. This problem is solved in the probabilistic approach. We combine these two approaches above by constructing a model which is both linear in the model parameters and probabilistic, thus allowing maximum margin training with calibrated outputs. Our model assumes that classes correspond to linear sub-spaces (rather than to half spaces), a view which is closely related to concepts in quantum detection theory. The corresponding optimization problems are semidefinite programs which can be solved efficiently. We illustrate the performance of our algorithm on real world datasets, and show that it outperforms second-order kernel methods.

AB - Discriminative linear models are a popular tool in machine learning. These can be generally divided into two types: linear classifiers, such as support vector machines (SVMs), which are well studied and provide state-of-the-art results, and probabilistic models such as logistic regression. One shortcoming of SVMs is that their output (known as the "margin") is not calibrated, so that it is difficult to incorporate such models as components of larger systems. This problem is solved in the probabilistic approach. We combine these two approaches above by constructing a model which is both linear in the model parameters and probabilistic, thus allowing maximum margin training with calibrated outputs. Our model assumes that classes correspond to linear sub-spaces (rather than to half spaces), a view which is closely related to concepts in quantum detection theory. The corresponding optimization problems are semidefinite programs which can be solved efficiently. We illustrate the performance of our algorithm on real world datasets, and show that it outperforms second-order kernel methods.

UR - http://www.scopus.com/inward/record.url?scp=80053210785&partnerID=8YFLogxK

M3 - פרסום בספר כנס

AN - SCOPUS:80053210785

SN - 0974903922

SN - 9780974903927

T3 - Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006

SP - 98

EP - 105

BT - Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006

Y2 - 13 July 2006 through 16 July 2006

ER -