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» Detecting worm variants using machine learning
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169
Voted
ML
2002
ACM
178views Machine Learning» more  ML 2002»
15 years 2 months ago
Metric-Based Methods for Adaptive Model Selection and Regularization
We present a general approach to model selection and regularization that exploits unlabeled data to adaptively control hypothesis complexity in supervised learning tasks. The idea ...
Dale Schuurmans, Finnegan Southey
104
Voted
CIDM
2007
IEEE
15 years 6 months ago
Efficient Kernel-based Learning for Trees
Kernel methods are effective approaches to the modeling of structured objects in learning algorithms. Their major drawback is the typically high computational complexity of kernel ...
Fabio Aiolli, Giovanni Da San Martino, Alessandro ...
136
Voted
ICMCS
2006
IEEE
131views Multimedia» more  ICMCS 2006»
15 years 8 months ago
Self-Supervised Learning for Robust Video Indexing
The performance of video analysis and indexing algorithms strongly depends on the type, content and recording characteristics of the analyzed video. Current video indexing approac...
Ralph Ewerth, Bernd Freisleben
122
Voted
MM
2005
ACM
172views Multimedia» more  MM 2005»
15 years 8 months ago
Learning the semantics of multimedia queries and concepts from a small number of examples
In this paper we unify two supposedly distinct tasks in multimedia retrieval. One task involves answering queries with a few examples. The other involves learning models for seman...
Apostol Natsev, Milind R. Naphade, Jelena Tesic
116
Voted
ICAI
2004
15 years 4 months ago
Inductive System Health Monitoring
- The Inductive Monitoring System (IMS) software was developed to provide a technique to automatically produce health monitoring knowledge bases for systems that are either difficu...
David L. Iverson