Dura R O, Hart P E, Stork D G. Pattern Classification. New York, USA: Wiley InterScience, 2000
[2]
Vapnik V N. The Nature of Statistical Learning Theory. New York, USA: Springer-Verlag, 1995
[3]
Lax P D. Functional Analysis. New York, USA: Wiley InterScience, 2002
[4]
Megiddo N. On the Complexity of Polyhedral Separability. Discrete & Computational Geometry, 1988, 3(1): 325-337
[5]
Tajine M, Elizondo D. New Methods for Testing Linear Separability. Neurocomputing, 2002, 47(1/2/3/4): 161-188
[6]
Ben-Israel A, Levin Y. The Geometry of Linear Separability in Data Sets. Linear Algebra and its Applications, 2006, 416(1): 75-87
[7]
Chen Degang, He Qiang, Wang Xizhao. On Linear Separability of Data Sets in Feature Space. Neurocomputing, 2007, 70(13/14/15): 2441-2448
[8]
Sun Y J. New Criteria for the Linear Binary Separability in the Euclidean Normed Space. The Open Cybernetics and Systemics Journal, 2008, 2: 101-105
[9]
Gabidullina Z R. A Linear Separability Criterion for Sets of Euclidean Space. Journal of Optimization Theory and Applications, 2013, 158(1): 145-171
[10]
Elizondo D. The Linear Separability Problem: Some Testing Methods. IEEE Trans on Neural Networks, 2006, 17(2): 330-344
[11]
Soliman M A, Abo-Bakr R M. Linearly and Quadratically Separable Classifiers Using Adaptive Approach. Journal of Computer Science and Technology, 2011, 26(5): 908-918
[12]
Aleksandrow A D, Kolmogorov A N, Lavrentev M A. Mathematics: Its Content, Methods, and Meaning. New York, USA: Dover Publications Inc., 1999
[13]
Elizondo D A, Rirkenhead R, Gamez M, et al. Linear Separability and Classification Complexity. Expert Systems with Applications, 2012, 39(9): 7796-7807
[14]
Schlkopf B, Smola A J. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, USA: The MIT Press, 2001
