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» Combining Methods for Dynamic Multiple Classifier Systems
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143
Voted
SMC
2007
IEEE
156views Control Systems» more  SMC 2007»
15 years 10 months ago
Dynamic fusion of classifiers for fault diagnosis
—This paper considers the problem of temporally fusing classifier outputs to improve the overall diagnostic classification accuracy in safety-critical systems. Here, we discuss d...
Satnam Singh, Kihoon Choi, Anuradha Kodali, Krishn...
127
Voted
IJCAI
1997
15 years 5 months ago
Combining Knowledge Acquisition and Machine Learning to Control Dynamic Systems
This paper presents an interactive method for building a controller for dynamic systems by using a combination of knowledge acquisition and machine learning techniques. The aim is...
G. M. Shiraz, Claude Sammut
137
Voted
SDM
2004
SIAM
211views Data Mining» more  SDM 2004»
15 years 5 months ago
Using Support Vector Machines for Classifying Large Sets of Multi-Represented Objects
Databases are a key technology for molecular biology which is a very data intensive discipline. Since molecular biological databases are rather heterogeneous, unification and data...
Hans-Peter Kriegel, Peer Kröger, Alexey Pryak...
163
Voted
BMCBI
2006
158views more  BMCBI 2006»
15 years 3 months ago
Parallelization of multicategory support vector machines (PMC-SVM) for classifying microarray data
Background: Multicategory Support Vector Machines (MC-SVM) are powerful classification systems with excellent performance in a variety of data classification problems. Since the p...
Chaoyang Zhang, Peng Li, Arun Rajendran, Youping D...
137
Voted
ECCV
2010
Springer
15 years 3 months ago
MIForests: Multiple-Instance Learning with Randomized Trees
Abstract. Multiple-instance learning (MIL) allows for training classifiers from ambiguously labeled data. In computer vision, this learning paradigm has been recently used in many ...
Christian Leistner, Amir Saffari, Horst Bischof