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» A Bayesian Metric for Evaluating Machine Learning Algorithms
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ML
2000
ACM
105views Machine Learning» more  ML 2000»
14 years 9 months ago
Multiple Comparisons in Induction Algorithms
Abstract. A single mechanism is responsible for three pathologies of induction algorithms: attribute selection errors, overfitting, and oversearching. In each pathology, induction ...
David D. Jensen, Paul R. Cohen
ICASSP
2008
IEEE
15 years 4 months ago
Discriminative learning for optimizing detection performance in spoken language recognition
We propose novel approaches for optimizing the detection performance in spoken language recognition. Two objective functions are designed to directly relate model parameters to tw...
Donglai Zhu, Haizhou Li, Bin Ma, Chin-Hui Lee
ICML
2006
IEEE
15 years 10 months ago
Efficient lazy elimination for averaged one-dependence estimators
Semi-naive Bayesian classifiers seek to retain the numerous strengths of naive Bayes while reducing error by weakening the attribute independence assumption. Backwards Sequential ...
Fei Zheng, Geoffrey I. Webb
ECIR
2010
Springer
14 years 11 months ago
Query Difficulty Prediction for Contextual Image Retrieval
Abstract. This paper explores how to predict query difficulty for contextual image retrieval. We reformulate the problem as the task of predicting how difficult to represent a quer...
Xing Xing, Yi Zhang 0001, Mei Han
ECML
2006
Springer
15 years 1 months ago
An Efficient Approximation to Lookahead in Relational Learners
Abstract. Greedy machine learning algorithms suffer from shortsightedness, potentially returning suboptimal models due to limited exploration of the search space. Greedy search mis...
Jan Struyf, Jesse Davis, C. David Page Jr.