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TNN
2010
176views Management» more  TNN 2010»
15 years 1 months ago
Sparse approximation through boosting for learning large scale kernel machines
Abstract--Recently, sparse approximation has become a preferred method for learning large scale kernel machines. This technique attempts to represent the solution with only a subse...
Ping Sun, Xin Yao
TNN
2010
159views Management» more  TNN 2010»
15 years 1 months ago
Multiple incremental decremental learning of support vector machines
We propose a multiple incremental decremental algorithm of Support Vector Machine (SVM). Conventional single incremental decremental SVM can update the trained model efficiently w...
Masayuki Karasuyama, Ichiro Takeuchi
ICASSP
2011
IEEE
14 years 10 months ago
Improving kernel-energy trade-offs for machine learning in implantable and wearable biomedical applications
Emerging biomedical sensors and stimulators offer unprecedented modalities for delivering therapy and acquiring physiological signals (e.g., deep brain stimulators). Exploiting th...
Kyong-Ho Lee, Sun-Yuan Kung, Naveen Verma
JCS
2011
138views more  JCS 2011»
14 years 9 months ago
Automatic analysis of malware behavior using machine learning
Malicious software—so called malware—poses a major threat to the security of computer systems. The amount and diversity of its variants render classic security defenses ineffe...
Konrad Rieck, Philipp Trinius, Carsten Willems, Th...
ICMLA
2008
15 years 8 months ago
Tumor Targeting for Lung Cancer Radiotherapy Using Machine Learning Techniques
Accurate lung tumor targeting in real time plays a fundamental role in image-guide radiotherapy of lung cancers. Precise tumor targeting is required for both respiratory gating an...
Tong Lin, Laura Cervino, Xiaoli Tang, Nuno Vasconc...