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MLDM
2007
Springer
13 years 10 months ago
PE-PUC: A Graph Based PU-Learning Approach for Text Classification
This paper presents a novel solution for the problem of building text classifier using positive documents (P) and unlabeled documents (U). Here, the unlabeled documents are mixed w...
Shuang Yu, Chunping Li
SIGIR
2008
ACM
13 years 4 months ago
Classifiers without borders: incorporating fielded text from neighboring web pages
Accurate web page classification often depends crucially on information gained from neighboring pages in the local web graph. Prior work has exploited the class labels of nearby p...
Xiaoguang Qi, Brian D. Davison
AIME
2009
Springer
13 years 11 months ago
Segmentation of Text and Non-text in On-Line Handwritten Patient Record Based on Spatio-Temporal Analysis
Note taking is a common way for physicians to collect information from their patients in medical inquiries and diagnoses. Many times, when describing the pathology in medical recor...
Rattapoom Waranusast, Peter Haddawy, Matthew N. Da...
BMCBI
2008
173views more  BMCBI 2008»
13 years 4 months ago
Extraction of semantic biomedical relations from text using conditional random fields
Background: The increasing amount of published literature in biomedicine represents an immense source of knowledge, which can only efficiently be accessed by a new generation of a...
Markus Bundschus, Mathäus Dejori, Martin Stet...
ICML
2003
IEEE
14 years 5 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