Feature space interpretation of SVMs with indefinite kernels
نویسندگان
چکیده
منابع مشابه
Feature Space Interpretation of SVMs with non Positive Definite Kernels
The widespread habit of “plugging” arbitrary symmetric functions as kernels in support vector machines (SVMs) often yields good empirical classification results. However, in case of non conditionally positive definite (non-cpd) functions they are hard to interpret due to missing geometrical and theoretical understanding. In this paper we provide a step towards comprehension of SVM classifiers i...
متن کاملFeature Space Restructuring for SVMs with Application to Text Categorization
In this paper, we propose a new method of text categorization based on feature space restructuring for SVMs. In our method, independent components of document vectors are extracted using ICA and concatenated with the original vectors. This restructuring makes it possible for SVMs to focus on the latent semantic space without losing information given by the original feature space. Using this met...
متن کاملAnalysis of SVM with Indefinite Kernels
The recent introduction of indefinite SVM by Luss and d’Aspremont [15] has effectively demonstrated SVM classification with a non-positive semi-definite kernel (indefinite kernel). This paper studies the properties of the objective function introduced there. In particular, we show that the objective function is continuously differentiable and its gradient can be explicitly computed. Indeed, we ...
متن کاملSupport vector machines with indefinite kernels
Training support vector machines (SVM) with indefinite kernels has recently attracted attention in the machine learning community. This is partly due to the fact that many similarity functions that arise in practice are not symmetric positive semidefinite, i.e. the Mercer condition is not satisfied, or the Mercer condition is difficult to verify. Previous work on training SVM with indefinite ke...
متن کاملSupport Vector Machine Classification with Indefinite Kernels
We propose a method for support vector machine classification using indefinite kernels. Instead of directly minimizing or stabilizing a nonconvex loss function, our algorithm simultaneously computes support vectors and a proxy kernel matrix used in forming the loss. This can be interpreted as a penalized kernel learning problem where indefinite kernel matrices are treated as a noisy observation...
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: IEEE Transactions on Pattern Analysis and Machine Intelligence
سال: 2005
ISSN: 0162-8828
DOI: 10.1109/tpami.2005.78