Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
SIAM Journal on Matrix Analysis and Applications
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Using Uncorrelated Discriminant Analysis for Tissue Classification with Gene Expression Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
SIAM Journal on Matrix Analysis and Applications
Robust and Accurate Cancer Classification with Gene Expression Profiling
CSB '05 Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference
Multiclass Classification with Multi-Prototype Support Vector Machines
The Journal of Machine Learning Research
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Rapid and brief communication: Face recognition based on 2D Fisherface approach
Pattern Recognition
Brief communication: Reducing multiclass cancer classification to binary by output coding and SVM
Computational Biology and Chemistry
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Generalizing discriminant analysis using the generalized singular value decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Binary tree of SVM: a new fast multiclass training and classification algorithm
IEEE Transactions on Neural Networks
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The classification of serum samples based on mass spectrometry (MS) has been increasingly used for monitoring disease progression and for diagnosing early disease. However, the classification task in mass spectrometry data is extremely challenging due to the very huge size of peaks (features) on mass spectra. Linear discriminant analysis (LDA) has been widely used for dimension reduction and feature extraction in many applications. However, the conversional LDA suffers from the singularity problem when dealing with high-dimensional features. Another critical limitation is its linearity property which results in failing in classification problems over nonlinearly clustered data sets. To overcome such problems, we develop a new fast kernel discriminant analysis (FKDA) that is pretty fast in the calculation of optimal discriminant vectors. FKDA is applied to the classification of liver cancer mass spectrometry data that consist of three categories: hepatocellular carcinoma, cirrhosis, and healthy that was originally analyzed by Ressom et al. [CHECK END OF SENTENCE]. We demonstrate the superiority and effectiveness of FKDA when compared to other classification techniques.