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» Learning to Classify Texts Using Positive and Unlabeled Data
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ICML
2003
IEEE
15 years 10 months ago
Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions
An approach to semi-supervised learning is proposed that is based on a Gaussian random field model. Labeled and unlabeled data are represented as vertices in a weighted graph, wit...
Xiaojin Zhu, Zoubin Ghahramani, John D. Lafferty
AUSDM
2006
Springer
144views Data Mining» more  AUSDM 2006»
15 years 1 months ago
A Characterization of Wordnet Features in Boolean Models For Text Classification
Supervised text classification is the task of automatically assigning a category label to a previously unlabeled text document. We start with a collection of pre-labeled examples ...
Trevor N. Mansuy, Robert J. Hilderman
91
Voted
SIGIR
2010
ACM
14 years 10 months ago
SED: supervised experimental design and its application to text classification
In recent years, active learning methods based on experimental design achieve state-of-the-art performance in text classification applications. Although these methods can exploit ...
Yi Zhen, Dit-Yan Yeung
74
Voted
ICONIP
2009
14 years 7 months ago
Exploring Early Classification Strategies of Streaming Data with Delayed Attributes
In contrast to traditional machine learning algorithms, where all data are available in batch mode, the new paradigm of streaming data poses additional difficulties, since data sam...
Mónica Millán-Giraldo, J. Salvador S...
ICPR
2002
IEEE
15 years 10 months ago
Incorporating Conditional Independence Assumption with Support Vector Machines to Enhance Handwritten Character Segmentation Per
Learning Bayesian Belief Networks (BBN) from corpora and incorporating the extracted inferring knowledge with a Support Vector Machines (SVM) classifier has been applied to charac...
Manolis Maragoudakis, Ergina Kavallieratou, Nikos ...