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» Support Vector Regression Using Mahalanobis Kernels
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ICONIP
2008
13 years 10 months ago
An Evaluation of Machine Learning-Based Methods for Detection of Phishing Sites
In this paper, we present the performance of machine learning-based methods for detection of phishing sites. We employ 9 machine learning techniques including AdaBoost, Bagging, S...
Daisuke Miyamoto, Hiroaki Hazeyama, Youki Kadobaya...
SDM
2009
SIAM
119views Data Mining» more  SDM 2009»
14 years 6 months ago
Twin Vector Machines for Online Learning on a Budget.
This paper proposes Twin Vector Machine (TVM), a constant space and sublinear time Support Vector Machine (SVM) algorithm for online learning. TVM achieves its favorable scaling b...
Zhuang Wang, Slobodan Vucetic
SSPR
2010
Springer
13 years 7 months ago
Information Theoretical Kernels for Generative Embeddings Based on Hidden Markov Models
Many approaches to learning classifiers for structured objects (e.g., shapes) use generative models in a Bayesian framework. However, state-of-the-art classifiers for vectorial d...
André F. T. Martins, Manuele Bicego, Vittor...
FPGA
2005
ACM
195views FPGA» more  FPGA 2005»
14 years 2 months ago
Sparse Matrix-Vector multiplication on FPGAs
Floating-point Sparse Matrix-Vector Multiplication (SpMXV) is a key computational kernel in scientific and engineering applications. The poor data locality of sparse matrices sig...
Ling Zhuo, Viktor K. Prasanna
ICIP
2010
IEEE
13 years 6 months ago
Combining free energy score spaces with information theoretic kernels: Application to scene classification
Most approaches to learn classifiers for structured objects (e.g., images) use generative models in a classical Bayesian framework. However, state-of-the-art classifiers for vecto...
Manuele Bicego, Alessandro Perina, Vittorio Murino...