Sciweavers

5122 search results - page 891 / 1025
» Comparing Consequence Relations
Sort
View
KDD
2009
ACM
180views Data Mining» more  KDD 2009»
16 years 4 months ago
Using graph-based metrics with empirical risk minimization to speed up active learning on networked data
Active and semi-supervised learning are important techniques when labeled data are scarce. Recently a method was suggested for combining active learning with a semi-supervised lea...
Sofus A. Macskassy
KDD
2009
ACM
152views Data Mining» more  KDD 2009»
16 years 4 months ago
A multi-relational approach to spatial classification
Spatial classification is the task of learning models to predict class labels based on the features of entities as well as the spatial relationships to other entities and their fe...
Richard Frank, Martin Ester, Arno Knobbe
KDD
2009
ACM
132views Data Mining» more  KDD 2009»
16 years 4 months ago
Learning patterns in the dynamics of biological networks
Our dynamic graph-based relational mining approach has been developed to learn structural patterns in biological networks as they change over time. The analysis of dynamic network...
Chang Hun You, Lawrence B. Holder, Diane J. Cook
KDD
2007
ACM
160views Data Mining» more  KDD 2007»
16 years 4 months ago
Show me the money!: deriving the pricing power of product features by mining consumer reviews
The increasing pervasiveness of the Internet has dramatically changed the way that consumers shop for goods. Consumergenerated product reviews have become a valuable source of inf...
Nikolay Archak, Anindya Ghose, Panagiotis G. Ipeir...
KDD
2006
ACM
134views Data Mining» more  KDD 2006»
16 years 4 months ago
Learning to rank networked entities
Several algorithms have been proposed to learn to rank entities modeled as feature vectors, based on relevance feedback. However, these algorithms do not model network connections...
Alekh Agarwal, Soumen Chakrabarti, Sunny Aggarwal