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» Computing LTS Regression for Large Data Sets
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PODS
2006
ACM
95views Database» more  PODS 2006»
14 years 7 months ago
Randomized computations on large data sets: tight lower bounds
We study the randomized version of a computation model (introduced in [9, 10]) that restricts random access to external memory and internal memory space. Essentially, this model c...
André Hernich, Martin Grohe, Nicole Schweik...
VLDB
2005
ACM
73views Database» more  VLDB 2005»
14 years 1 months ago
Maximal Vector Computation in Large Data Sets
Parke Godfrey, Ryan Shipley, Jarek Gryz
KDD
2004
ACM
132views Data Mining» more  KDD 2004»
14 years 8 months ago
Privacy preserving regression modelling via distributed computation
Reluctance of data owners to share their possibly confidential or proprietary data with others who own related databases is a serious impediment to conducting a mutually beneficia...
Ashish P. Sanil, Alan F. Karr, Xiaodong Lin, Jerom...
ICDM
2006
IEEE
146views Data Mining» more  ICDM 2006»
14 years 1 months ago
Boosting Kernel Models for Regression
This paper proposes a general boosting framework for combining multiple kernel models in the context of both classification and regression problems. Our main approach is built on...
Ping Sun, Xin Yao
KDD
2003
ACM
180views Data Mining» more  KDD 2003»
14 years 8 months ago
Classifying large data sets using SVMs with hierarchical clusters
Support vector machines (SVMs) have been promising methods for classification and regression analysis because of their solid mathematical foundations which convey several salient ...
Hwanjo Yu, Jiong Yang, Jiawei Han