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JMLR
2012
13 years 9 months ago
Online Incremental Feature Learning with Denoising Autoencoders
While determining model complexity is an important problem in machine learning, many feature learning algorithms rely on cross-validation to choose an optimal number of features, ...
Guanyu Zhou, Kihyuk Sohn, Honglak Lee
SIGIR
2009
ACM
16 years 1 months ago
Smoothing clickthrough data for web search ranking
Incorporating features extracted from clickthrough data (called clickthrough features) has been demonstrated to significantly improve the performance of ranking models for Web sea...
Jianfeng Gao, Wei Yuan, Xiao Li, Kefeng Deng, Jian...
KDD
2000
ACM
121views Data Mining» more  KDD 2000»
15 years 10 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
SAC
2005
ACM
16 years 6 days ago
Rearranging data objects for efficient and stable clustering
When a partitional structure is derived from a data set using a data mining algorithm, it is not unusual to have a different set of outcomes when it runs with a different order of...
Gyesung Lee, Xindong Wu, Jinho Chon
BTW
2005
Springer
113views Database» more  BTW 2005»
16 years 6 days ago
A Learning Optimizer for a Federated Database Management System
: Optimizers in modern DBMSs utilize a cost model to choose an efficient query execution plan (QEP) among all possible ones for a given query. The accuracy of the cost estimates de...
Stephan Ewen, Michael Ortega-Binderberger, Volker ...