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JMLR
2006
132views more  JMLR 2006»
15 years 4 months ago
Learning to Detect and Classify Malicious Executables in the Wild
We describe the use of machine learning and data mining to detect and classify malicious executables as they appear in the wild. We gathered 1,971 benign and 1,651 malicious execu...
Jeremy Z. Kolter, Marcus A. Maloof
AAAI
2012
13 years 6 months ago
A Testbed for Learning by Demonstration from Natural Language and RGB-Depth Video
We are developing a testbed for learning by demonstration combining spoken language and sensor data in a natural real-world environment. Microsoft Kinect RGBDepth cameras allow us...
Young Chol Song, Henry A. Kautz
BMCBI
2011
14 years 7 months ago
Learning sparse models for a dynamic Bayesian network classifier of protein secondary structure
Background: Protein secondary structure prediction provides insight into protein function and is a valuable preliminary step for predicting the 3D structure of a protein. Dynamic ...
Zafer Aydin, Ajit Singh, Jeff Bilmes, William Staf...
IJCAT
2010
133views more  IJCAT 2010»
15 years 2 months ago
A 3D shape classifier with neural network supervision
: The task of 3D shape classification is to assign a set of unordered shapes into pre-tagged classes with class labels, and find the most suitable class for a newly given shape. In...
Zhenbao Liu, Jun Mitani, Yukio Fukui, Seiichi Nish...
ACCV
2009
Springer
15 years 10 months ago
Transductive Segmentation of Textured Meshes
This paper addresses the problem of segmenting a textured mesh into objects or object classes, consistently with user-supplied seeds. We view this task as transductive learning and...
Anne-Laure Chauve, Jean-Philippe Pons, Jean-Yves A...