C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Solving the quadratic programming problem arising in support vector classification
Advances in kernel methods
Making large-scale support vector machine learning practical
Advances in kernel methods
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Machine Learning
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
A semi-supervised regression model for mixed numerical and categorical variables
Pattern Recognition
Computational Statistics & Data Analysis
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Knowledge discovery approach to automated cardiac SPECT diagnosis
Artificial Intelligence in Medicine
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
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Incorporating various sources of biological information is important for biological discovery. For example, genes have a multiview representation. They can be represented by features such as sequence length and pairwise similarities. Hence, the types vary from numerical features to categorical features. We propose a large margin Random Forests (RF) classification approach based on RF proximity kernals. Random Forests accommodate mixed data types naturally. The performance on four biological datasets is promising compared with other state of the art methods including Support Vector Machines (SVMs) and RF classifiers. It demonstrates high potential in the discovery of functional roles of biomolecules.