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96
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PKDD
1999
Springer
140views Data Mining» more  PKDD 1999»
15 years 5 months ago
Learning of Simple Conceptual Graphs from Positive and Negative Examples
Sergei O. Kuznetsov
102
Voted
ICSE
2009
IEEE-ACM
16 years 1 months ago
Learning operational requirements from goal models
Goal-oriented methods have increasingly been recognised as an effective means for eliciting, elaborating, analysing and specifying software requirements. A key activity in these a...
Alessandra Russo, Dalal Alrajeh, Jeff Kramer, Seba...
107
Voted
IJCNN
2000
IEEE
15 years 5 months ago
Extracting Distributed Representations of Concepts and Relations from Positive and Negative Propositions
Linear Relational Embedding (LRE) was introduced (Paccanaro and Hinton, 1999) as a means of extracting a distributed representation of concepts from relational data. The original ...
Alberto Paccanaro, Geoffrey E. Hinton
117
Voted
WAIM
2010
Springer
15 years 6 months ago
Semi-supervised Learning from Only Positive and Unlabeled Data Using Entropy
Abstract. The problem of classification from positive and unlabeled examples attracts much attention currently. However, when the number of unlabeled negative examples is very sma...
Xiaoling Wang, Zhen Xu, Chaofeng Sha, Martin Ester...
GECCO
2005
Springer
158views Optimization» more  GECCO 2005»
15 years 6 months ago
Applying both positive and negative selection to supervised learning for anomaly detection
This paper presents a novel approach of applying both positive selection and negative selection to supervised learning for anomaly detection. It first learns the patterns of the n...
Xiaoshu Hang, Honghua Dai