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ICASSP
2009
IEEE
15 years 1 months ago
High-level feature extraction using SVM with walk-based graph kernel
We investigate a method using support vector machines (SVMs) with walk-based graph kernels for high-level feature extraction from images. In this method, each image is first segme...
Jean-Philippe Vert, Tomoko Matsui, Shin'ichi Satoh...
TJS
2010
182views more  TJS 2010»
14 years 8 months ago
A novel unsupervised classification approach for network anomaly detection by k-Means clustering and ID3 decision tree learning
This paper presents a novel host-based combinatorial method based on k-Means clustering and ID3 decision tree learning algorithms for unsupervised classification of anomalous and ...
Yasser Yasami, Saadat Pour Mozaffari
ICIP
2008
IEEE
15 years 11 months ago
Using local regression kernels for statistical object detection
We present a novel approach to the problem of detection of visual similarity between a template image, and patches in a given image. The method is based on the computation of a lo...
Hae Jong Seo, Peyman Milanfar
86
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INFOCOM
2007
IEEE
15 years 3 months ago
Multivariate Online Anomaly Detection Using Kernel Recursive Least Squares
— High-speed backbones are regularly affected by various kinds of network anomalies, ranging from malicious attacks to harmless large data transfers. Different types of anomalies...
Tarem Ahmed, Mark Coates, Anukool Lakhina
SNPD
2004
14 years 11 months ago
Using extended phylogenetc profiles and support vector machines for protein family classification
We proposed a new approach to compare profiles when the correlations among attributes can be represented as a tree. To account for these correlations, the profile is extended with...
Kishore Narra, Li Liao