Machine Learning - Special issue on applications in molecular biology
Making large-scale support vector machine learning practical
Advances in kernel methods
Multispace KL for Pattern Representation and Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
New techniques for extracting features from protein sequences
IBM Systems Journal - Deep computing for the life sciences
Classification Probability Analysis of Principal Component Null Space Analysis
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Principal components null space analysis for image and video classification
IEEE Transactions on Image Processing
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The protein family classification problem, which consists of determining the family memberships of given unknown protein sequences, is very important for a biologist for many practical reasons, such as drug discovery, prediction of molecular functions and medical diagnosis Neural networks and bayesian methods have performed well on the protein classification problem, achieving accuracy ranging from 90% to 98% while running relatively slowly in the learning stage In this paper, we present a principal component null space analysis (PCNSA) linear classifier to the problem and report excellent results compared to those of neural networks and support vector machines The two main parameters of PCNSA are linked to the high dimensionality of the dataset used, and were optimized in an exhaustive manner to maximize accuracy.