منابع مشابه
Max-Margin feature selection
Many machine learning applications such as in vision, biology and social networking deal with data in high dimensions. Feature selection is typically employed to select a subset of features which improves generalization accuracy as well as reduces the computational cost of learning the model. One of the criteria used for feature selection is to jointly minimize the redundancy and maximize the r...
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Embedding feature selection in nonlinear SVMs leads to a challenging non-convex minimization problem, which can be prone to suboptimal solutions. This paper develops an effective algorithm to directly solve the embedded feature selection primal problem. We use a trust-region method, which is better suited for non-convex optimization compared to line-search methods, and guarantees convergence to...
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Segmenting a user-specified foreground object in video sequences has received considerable attention over the past decade. State-ofthe-art methods propose the use of multiple cues other than color in order to discriminate foreground from background. These multiple features are combined within a graph-cut optimization framework and segmentation is predominantly performed on a frame by frame basi...
متن کاملKernel-based Joint Feature Selection and Max-Margin Classification for Early Diagnosis of Parkinson’s Disease
Feature selection methods usually select the most compact and relevant set of features based on their contribution to a linear regression model. Thus, these features might not be the best for a non-linear classifier. This is especially crucial for the tasks, in which the performance is heavily dependent on the feature selection techniques, like the diagnosis of neurodegenerative diseases. Parki...
متن کاملExplicit Max Margin Input Feature Selection for Nonlinear SVM using Second Order Methods
Incorporating feature selection in nonlinear SVMs leads to a large and challenging nonconvex minimization problem, which can be prone to suboptimal solutions. We use a second order optimization method that utilizes eigenvalue information and is less likely to get stuck at suboptimal solutions. We devise an alternating optimization approach to tackle the problem efficiently, breaking it down int...
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ژورنال
عنوان ژورنال: Pattern Recognition Letters
سال: 2017
ISSN: 0167-8655
DOI: 10.1016/j.patrec.2017.04.011