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KDD
1995
ACM
129views Data Mining» more  KDD 1995»
13 years 11 months ago
Feature Extraction for Massive Data Mining
Techniques for learning from data typically require data to be in standard form. Measurements must be encoded in a numerical format such as binary true-or-false features, numerica...
V. Seshadri, Raguram Sasisekharan, Sholom M. Weiss
DGO
2007
174views Education» more  DGO 2007»
13 years 9 months ago
A bootstrapping approach for identifying stakeholders in public-comment corpora
A stakeholder is an individual, group, organization, or community that has an interest or stake in a consensus-building process. The goal of stakeholder identification is identify...
Jaime Arguello, Jamie Callan
NAACL
1994
13 years 8 months ago
Issues and Methodology for Template Design for Information Extraction
The goal of Information Extraction tasks is to identify, categorize, classify, relate, and normalize specific information of interest found in free text, and to make that informat...
Boyan A. Onyshkevych
JMLR
2008
148views more  JMLR 2008»
13 years 7 months ago
Linear-Time Computation of Similarity Measures for Sequential Data
Efficient and expressive comparison of sequences is an essential procedure for learning with sequential data. In this article we propose a generic framework for computation of sim...
Konrad Rieck, Pavel Laskov
JMLR
2006
125views more  JMLR 2006»
13 years 7 months ago
Spam Filtering Using Statistical Data Compression Models
Spam filtering poses a special problem in text categorization, of which the defining characteristic is that filters face an active adversary, which constantly attempts to evade fi...
Andrej Bratko, Gordon V. Cormack, Bogdan Filipic, ...