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» On the monotonization of the training set
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SIGIR
2012
ACM
13 years 8 months ago
Parallelizing ListNet training using spark
As ever-larger training sets for learning to rank are created, scalability of learning has become increasingly important to achieving continuing improvements in ranking accuracy [...
Shilpa Shukla, Matthew Lease, Ambuj Tewari
ICML
2007
IEEE
16 years 7 months ago
Asymptotic Bayesian generalization error when training and test distributions are different
In supervised learning, we commonly assume that training and test data are sampled from the same distribution. However, this assumption can be violated in practice and then standa...
Keisuke Yamazaki, Klaus-Robert Müller, Masash...
IJCNN
2006
IEEE
16 years 6 days ago
Divide and Conquer Strategies for MLP Training
— Over time, neural networks have proven to be extremely powerful tools for data exploration with the capability to discover previously unknown dependencies and relationships in ...
Smriti Bhagat, Dipti Deodhare
PCM
2004
Springer
127views Multimedia» more  PCM 2004»
15 years 11 months ago
Using a Non-prior Training Active Feature Model
This paper presents a feature point tracking algorithm using optical flow under the non-prior training active feature model (NPTAFM) framework. The proposed algorithm mainly focus...
Sangjin Kim, Jinyoung Kang, Jeongho Shin, Seongwon...
CIVR
2003
Springer
107views Image Analysis» more  CIVR 2003»
15 years 11 months ago
Fast Video Retrieval under Sparse Training Data
Feature selection for video retrieval applications is impractical with existing techniques, because of their high time complexity and their failure on the relatively sparse trainin...
Yan Liu, John R. Kender