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KDD
2005
ACM
149views Data Mining» more  KDD 2005»
13 years 10 months ago
A distributed learning framework for heterogeneous data sources
We present a probabilistic model-based framework for distributed learning that takes into account privacy restrictions and is applicable to scenarios where the different sites ha...
Srujana Merugu, Joydeep Ghosh
SIGIR
2012
ACM
11 years 7 months ago
Learning to suggest: a machine learning framework for ranking query suggestions
We consider the task of suggesting related queries to users after they issue their initial query to a web search engine. We propose a machine learning approach to learn the probab...
Umut Ozertem, Olivier Chapelle, Pinar Donmez, Emre...
KDD
2006
ACM
180views Data Mining» more  KDD 2006»
14 years 5 months ago
Learning the unified kernel machines for classification
Kernel machines have been shown as the state-of-the-art learning techniques for classification. In this paper, we propose a novel general framework of learning the Unified Kernel ...
Steven C. H. Hoi, Michael R. Lyu, Edward Y. Chang
PKDD
2004
Springer
324views Data Mining» more  PKDD 2004»
13 years 10 months ago
Orange: From Experimental Machine Learning to Interactive Data Mining
Abstract. Orange (www.ailab.si/orange) is a suite for machine learning and data mining. It can be used though scripting in Python or with visual programming in Orange Canvas using ...
Janez Demsar, Blaz Zupan, Gregor Leban, Tomaz Curk
SDM
2008
SIAM
177views Data Mining» more  SDM 2008»
13 years 6 months ago
Practical Private Computation and Zero-Knowledge Tools for Privacy-Preserving Distributed Data Mining
In this paper we explore private computation built on vector addition and its applications in privacypreserving data mining. Vector addition is a surprisingly general tool for imp...
Yitao Duan, John F. Canny