Multicategory Proximal Support Vector Machine Classifiers

نویسندگان
چکیده

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Multicategory Incremental Proximal Support Vector Classifiers

Support Vector Machines (SVMs) are an efficient data mining approach for classification, clustering and time series analysis. In recent years, a tremendous growth in the amount of data gathered has changed the focus of SVM classifier algorithms from providing accurate results to enabling incremental (and decremental) learning with new data (or unlearning old data) without the need for computati...

متن کامل

Multicategory Support Vector Machines

The Support Vector Machine (SVM) has shown great performance in practice as a classification methodology. Oftentimes multicategory problems have been treated as a series of binary problems in the SVM paradigm. Even though the SVM implements the optimal classification rule asymptotically in the binary case, solutions to a series of binary problems may not be optimal for the original multicategor...

متن کامل

Multicategory ψ-Learning and Support Vector Machine: Computational Tools

Many margin-based binary classification techniques such as support vector machine (SVM) andψ-learning deliver high performance. An earlier article proposed a new multicategory ψ-learning methodology that shows great promise in generalization ability. However, ψ-learning is computationally difficult because it requires handling a nonconvex minimization problem. In this article, we propose two co...

متن کامل

Knowledge-Based Support Vector Machine Classifiers

Prior knowledge in the form of multiple polyhedral sets, each belonging to one of two categories, is introduced into a reformulation of a linear support vector machine classifier. The resulting formulation leads to a linear program that can be solved efficiently. Real world examples, from DNA sequencing and breast cancer prognosis, demonstrate the effectiveness of the proposed method. Numerical...

متن کامل

Sparse least squares Support Vector Machine classifiers

In least squares support vector machine (LS-SVM) classi-ers the original SVM formulation of Vapnik is modiied by considering equalit y constraints within a form of ridge regression instead of inequality constraints. As a result the solution follows from solving a set of linear equations instead of a quadratic programming problem. Ho wever, a d r a wback is that sparseness is lost in the LS-SVM ...

متن کامل

ذخیره 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: Machine Learning

سال: 2005

ISSN: 0885-6125,1573-0565

DOI: 10.1007/s10994-005-0463-6