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ICML
2002
IEEE
16 years 4 months ago
Adaptive View Validation: A First Step Towards Automatic View Detection
Multi-view algorithms reduce the amount of required training data by partitioning the domain features into separate subsets or views that are sufficient to learn the target concep...
Ion Muslea, Steven Minton, Craig A. Knoblock
150
Voted
ICMLA
2010
15 years 1 months ago
Boosting Multi-Task Weak Learners with Applications to Textual and Social Data
Abstract--Learning multiple related tasks from data simultaneously can improve predictive performance relative to learning these tasks independently. In this paper we propose a nov...
Jean Baptiste Faddoul, Boris Chidlovskii, Fabien T...
ICALT
2006
IEEE
15 years 9 months ago
Auto-Adaptive Questions in E-Learning System
All books entitled “Learn … with 1000 exercises” have in common the same basic principle. They aim to supply enough material to students so that they may better understand t...
Enrique Lazcorreta, Federico Botella, Antonio Fern...
157
Voted
ISCIS
2005
Springer
15 years 9 months ago
Classification of Volatile Organic Compounds with Incremental SVMs and RBF Networks
Support Vector Machines (SVMs) have been applied to solve the classification of volatile organic compounds (VOC) data in some recent studies. SVMs provide good generalization perfo...
Zeki Erdem, Robi Polikar, Nejat Yumusak, Fikret S....
141
Voted
HIS
2004
15 years 5 months ago
Adaptive Boosting with Leader based Learners for Classification of Large Handwritten Data
Boosting is a general method for improving the accuracy of a learning algorithm. AdaBoost, short form for Adaptive Boosting method, consists of repeated use of a weak or a base le...
T. Ravindra Babu, M. Narasimha Murty, Vijay K. Agr...