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» Scaling-Up Support Vector Machines Using Boosting Algorithm
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KDD
2004
ACM
330views Data Mining» more  KDD 2004»
16 years 2 months ago
Learning to detect malicious executables in the wild
In this paper, we describe the development of a fielded application for detecting malicious executables in the wild. We gathered 1971 benign and 1651 malicious executables and enc...
Jeremy Z. Kolter, Marcus A. Maloof
WWW
2010
ACM
15 years 9 months ago
Distributed nonnegative matrix factorization for web-scale dyadic data analysis on mapreduce
The Web abounds with dyadic data that keeps increasing by every single second. Previous work has repeatedly shown the usefulness of extracting the interaction structure inside dya...
Chao Liu, Hung-chih Yang, Jinliang Fan, Li-Wei He,...
ICML
2010
IEEE
15 years 3 months ago
Boosting Classifiers with Tightened L0-Relaxation Penalties
We propose a novel boosting algorithm which improves on current algorithms for weighted voting classification by striking a better balance between classification accuracy and the ...
Noam Goldberg, Jonathan Eckstein
IJCNN
2006
IEEE
15 years 8 months ago
Pattern Selection for Support Vector Regression based on Sparseness and Variability
— Support Vector Machine has been well received in machine learning community with its theoretical as well as practical value. However, since its training time complexity is cubi...
Jiyoung Sun, Sungzoon Cho
CVPR
2010
IEEE
15 years 7 months ago
Facial Point Detection using Boosted Regression and Graph Models
Finding fiducial facial points in any frame of a video showing rich naturalistic facial behaviour is an unsolved problem. Yet this is a crucial step for geometric-featurebased fa...
Michel Valstar, Brais Martinez, Xavier Binefa, Maj...