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ML
2006
ACM
142views Machine Learning» more  ML 2006»
15 years 5 months ago
The max-min hill-climbing Bayesian network structure learning algorithm
We present a new algorithm for Bayesian network structure learning, called Max-Min Hill-Climbing (MMHC). The algorithm combines ideas from local learning, constraint-based, and sea...
Ioannis Tsamardinos, Laura E. Brown, Constantin F....
IAJIT
2007
104views more  IAJIT 2007»
15 years 5 months ago
A Learning-Classification Based Approach for Word Prediction
: Word prediction is an important NLP problem in which we want to predict the correct word in a given context. Word completion utilities, predictive text entry systems, writing aid...
Hisham Al-Mubaid
SIGOPS
2008
156views more  SIGOPS 2008»
15 years 5 months ago
O2S2: enhanced object-based virtualized storage
Object based storage devices (OSDs) elevate the level of abstraction presented to clients, thereby permitting them to offer methods for managing, sharing, and securing information...
Himanshu Raj, Karsten Schwan
TSE
2008
129views more  TSE 2008»
15 years 5 months ago
Classifying Software Changes: Clean or Buggy?
This paper introduces a new technique for predicting latent software bugs, called change classification. Change classification uses a machine learning classifier to determine wheth...
Sunghun Kim, E. James Whitehead Jr., Yi Zhang 0001
JMLR
2007
101views more  JMLR 2007»
15 years 5 months ago
Noise Tolerant Variants of the Perceptron Algorithm
A large number of variants of the Perceptron algorithm have been proposed and partially evaluated in recent work. One type of algorithm aims for noise tolerance by replacing the l...
Roni Khardon, Gabriel Wachman