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» Evaluating algorithms that learn from data streams
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KDD
2012
ACM
187views Data Mining» more  KDD 2012»
13 years 17 days ago
Online learning to diversify from implicit feedback
In order to minimize redundancy and optimize coverage of multiple user interests, search engines and recommender systems aim to diversify their set of results. To date, these dive...
Karthik Raman, Pannaga Shivaswamy, Thorsten Joachi...
103
Voted
ISCC
2007
IEEE
115views Communications» more  ISCC 2007»
15 years 4 months ago
On The Use Data Reduction Algorithms for Real-Time Wireless Sensor Networks
This work presents the design of real-time applications for wireless sensor networks (WSNs) by using an algorithm based on data stream to process the sensor data. The proposed alg...
André L. L. de Aquino, Carlos Mauricio S. F...
ICDE
2009
IEEE
171views Database» more  ICDE 2009»
15 years 5 months ago
CoTS: A Scalable Framework for Parallelizing Frequency Counting over Data Streams
Applications involving analysis of data streams have gained significant popularity and importance. Frequency counting, frequent elements and top-k queries form a class of operato...
Sudipto Das, Shyam Antony, Divyakant Agrawal, Amr ...
KDD
2008
ACM
239views Data Mining» more  KDD 2008»
15 years 10 months ago
Mining adaptively frequent closed unlabeled rooted trees in data streams
Closed patterns are powerful representatives of frequent patterns, since they eliminate redundant information. We propose a new approach for mining closed unlabeled rooted trees a...
Albert Bifet, Ricard Gavaldà
ACL
2008
14 years 11 months ago
Learning Effective Multimodal Dialogue Strategies from Wizard-of-Oz Data: Bootstrapping and Evaluation
We address two problems in the field of automatic optimization of dialogue strategies: learning effective dialogue strategies when no initial data or system exists, and evaluating...
Verena Rieser, Oliver Lemon