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» Evaluating learning algorithms and classifiers
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ICML
2000
IEEE
16 years 4 months ago
Complete Cross-Validation for Nearest Neighbor Classifiers
Cross-validation is an established technique for estimating the accuracy of a classifier and is normally performed either using a number of random test/train partitions of the dat...
Matthew D. Mullin, Rahul Sukthankar
DMIN
2006
133views Data Mining» more  DMIN 2006»
15 years 5 months ago
A Fuzzy Neural Based Data Classification System
Data mining has emerged to be a very important research area that helps organizations make good use of the tremendous amount of data they have. In data classification tasks, fuzzy ...
Luong Trung Tuan, Suet Peng Yong
IFIP
2010
Springer
14 years 11 months ago
Combining Software and Hardware LCS for Lightweight On-Chip Learning
In this paper we present a novel two-stage method to realize a lightweight but very capable hardware implementation of a Learning Classifier System for on-chip learning. Learning C...
Andreas Bernauer, Johannes Zeppenfeld, Oliver Brin...
BMCBI
2006
151views more  BMCBI 2006»
15 years 4 months ago
Machine learning and word sense disambiguation in the biomedical domain: design and evaluation issues
Background: Word sense disambiguation (WSD) is critical in the biomedical domain for improving the precision of natural language processing (NLP), text mining, and information ret...
Hua Xu, Marianthi Markatou, Rositsa Dimova, Hongfa...
ICML
2006
IEEE
16 years 4 months ago
Agnostic active learning
We state and analyze the first active learning algorithm which works in the presence of arbitrary forms of noise. The algorithm, A2 (for Agnostic Active), relies only upon the ass...
Maria-Florina Balcan, Alina Beygelzimer, John Lang...