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» Evaluating algorithms that learn from data streams
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KDD
2000
ACM
121views Data Mining» more  KDD 2000»
13 years 9 months ago
Mining high-speed data streams
Many organizations today have more than very large databases; they have databases that grow without limit at a rate of several million records per day. Mining these continuous dat...
Pedro Domingos, Geoff Hulten
ACL
2012
11 years 8 months ago
Named Entity Disambiguation in Streaming Data
The named entity disambiguation task is to resolve the many-to-many correspondence between ambiguous names and the unique realworld entity. This task can be modeled as a classifi...
Alexandre Davis, Adriano Veloso, Altigran Soares d...
ICDE
2008
IEEE
141views Database» more  ICDE 2008»
14 years 7 months ago
SPOT: A System for Detecting Projected Outliers From High-dimensional Data Streams
In this paper, we present a new technique, called Stream Projected Ouliter deTector (SPOT), to deal with outlier detection problem in high-dimensional data streams. SPOT is unique ...
Ji Zhang, Qigang Gao, Hai H. Wang
DIS
2010
Springer
13 years 4 months ago
Sentiment Knowledge Discovery in Twitter Streaming Data
Micro-blogs are a challenging new source of information for data mining techniques. Twitter is a micro-blogging service built to discover what is happening at any moment in time, a...
Albert Bifet, Eibe Frank
ICML
2005
IEEE
14 years 6 months ago
Learning predictive representations from a history
Predictive State Representations (PSRs) have shown a great deal of promise as an alternative to Markov models. However, learning a PSR from a single stream of data generated from ...
Eric Wiewiora