Add to ORKGThe Minimax Probability Machine Classification (MPMC) framework [Lanckriet et al., 2002] builds classifiers by minimizing the maximum probability of misclassification, and gives direct estimates of the probabilistic accuracy bound# . The only assumptions that MPMC makes is that good estimates of means and covariance matrixes of the classes exist. However
This paper proposes a computationally ecient class of nonparametric binary classi cation algorithms ...
Supervised classification techniques use training samples to learn a classification rule with small...
Regularization techniques have become a principled tool for model-based statistics and artificial in...
The Minimax Probability Machine Classification (MPMC) framework [Lanckriet et al., 2002] builds cla...
© 2018 Elsevier Inc. Minimax Probability Machine (MPM) is a binary classifier that optimizes the upp...
When constructing a classifier, the probability of correct classification of future data points shou...
We formulate the regression problem as one of maximizing the minimum probability, symbolized by &...
We study the problem of designing support vector machine (SVM) classifiers that minimize the maximu...
Abstract—The challenging task of medical diagnosis based on machine learning techniques requires an ...
Abstract—Imbalanced learning is a challenged task in machine learning. In this context, the data ass...
Abstract—Probabilistic classification vector machine (PCVM) [5] is a sparse learning approach aiming...
We provide an exact nonasymptotic lower bound on the minimax expected excess risk (EER) in the agnos...
A new class of nonparametric algorithms for high-dimensional binary classification is proposed using...
This paper presents a novel method for simultaneous feature selection and classification by incorpor...
Supervised classification techniques use training samples to learn a classification rule with small ...
This paper proposes a computationally ecient class of nonparametric binary classi cation algorithms ...
Supervised classification techniques use training samples to learn a classification rule with small...
Regularization techniques have become a principled tool for model-based statistics and artificial in...
The Minimax Probability Machine Classification (MPMC) framework [Lanckriet et al., 2002] builds cla...
© 2018 Elsevier Inc. Minimax Probability Machine (MPM) is a binary classifier that optimizes the upp...
When constructing a classifier, the probability of correct classification of future data points shou...
We formulate the regression problem as one of maximizing the minimum probability, symbolized by &...
We study the problem of designing support vector machine (SVM) classifiers that minimize the maximu...
Abstract—The challenging task of medical diagnosis based on machine learning techniques requires an ...
Abstract—Imbalanced learning is a challenged task in machine learning. In this context, the data ass...
Abstract—Probabilistic classification vector machine (PCVM) [5] is a sparse learning approach aiming...
We provide an exact nonasymptotic lower bound on the minimax expected excess risk (EER) in the agnos...
A new class of nonparametric algorithms for high-dimensional binary classification is proposed using...
This paper presents a novel method for simultaneous feature selection and classification by incorpor...
Supervised classification techniques use training samples to learn a classification rule with small ...
This paper proposes a computationally ecient class of nonparametric binary classi cation algorithms ...
Supervised classification techniques use training samples to learn a classification rule with small...
Regularization techniques have become a principled tool for model-based statistics and artificial in...