Sequential minimal optimization for SVM with pinball loss
نویسندگان
چکیده
منابع مشابه
Sequential minimal optimization for SVM with pinball loss
To pursue the insensitivity to feature noise and the stability to re-sampling, a new type of support vector machine (SVM) has been established via replacing the hinge loss in the classical SVM by the pinball loss and was hence called a pin-SVM. Though a different loss function is used, pin-SVM has a similar structure as the classical SVM. Specifically, the dual problem of pin-SVM is a quadratic...
متن کاملSequential Labeling with Structural SVM Under an Average Precision Loss
The average precision (AP) is an important and widely-adopted performance measure for information retrieval and classification systems. However, owing to its relatively complex formulation, very few approaches have been proposed to learn a classifier by maximising its average precision over a given training set. Moreover, most of the existing work is restricted to i.i.d. data and does not exten...
متن کاملSequential labeling with structural SVM under the F1 loss
Sequential labeling addresses the classification of sequential data and is of increasing importance for the classification and segmentation of video data. The model traditionally used for sequential labeling is the hidden Markov model where the sequence of class labels to be predicted is encoded as a Markov chain. In recent years, hidden Markov models and other structural models have benefited ...
متن کاملFast Kernel Learning using Sequential Minimal Optimization
While classical kernel-based classifiers are based on a single kernel, in practice it is often desirable to base classifiers on combinations of multiple kernels. Lanckriet et al. (2004) considered conic combinations of kernel matrices for the support vector machine (SVM), and showed that the optimization of the coefficients of such a combination reduces to a convex optimization problem known as...
متن کاملMax-Margin Learning of Gaussian Mixtures with Sequential Minimal Optimization
This works deals with discriminant training of Gaussian Mixture Models through margin maximization. We go one step further previous work, we propose a new formulation of the learning problem that allows the use of efficient optimization algorithm popularized for Support Vector Machines, yielding improved convergence properties and recognition accuracy on handwritten digits recognition.
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Neurocomputing
سال: 2015
ISSN: 0925-2312
DOI: 10.1016/j.neucom.2014.08.033