MachLearn( Vol No. )
* *Machine Learning
MachLearn(1)
* Induction of Decision Trees
MachLearn(20)
* Support-Vector Networks
MachLearn(24)
* Bagging Predictors
MachLearn(29)
* Bayesian Network Classifiers
* On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
MachLearn(31)
* hierarchical hidden markov model: Analysis and applications, The
* Learning to Recognize and Grasp Objects
* Self-Calibration of the Fixation Movement of a Stereo Camera Head
* Training A Vision-Guided Mobile Robot
MachLearn(32)
* Robust Sensor Fusion: Analysis and Application to Audio-Visual Speech Recognition
MachLearn(37)
* Improved boosting algorithms using confidence-rated predictions
MachLearn(40)
* Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-Three Old and New Classification Algorithms, A
* Random Forests
MachLearn(45)
* Random Forests
MachLearn(51)
* Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy
MachLearn(58)
* Not So Naive Bayes: Aggregating One-Dependence Estimators
MachLearnRes( Vol No. )
* *Journal of Machine Learning Research
MachLearnRes(3)
* Kernel independent component analysis
MachLearnRes(4)
* Learning over sets using kernel principal angles
MachLearnRes(7)
* Direct Method for Building Sparse Kernel Learning Algorithms, A