Smith MA: Reptilia and Amphibia. Today & Tomorrow’s Printers & Publishers, India; 1981.
Google Scholar
Whitaker R, Captain A, Ahmed F: Snakes of India: the field guide. Draco Books, Chengalpattu; 2004.
Google Scholar
Mattison C: Snake. Dorling Kindersley, New York,USA; 1999.
Google Scholar
Firth SMJWJR: Snake. Scholastic, India; 2002.
Google Scholar
Weidensaul S: Snakes of the World. Grange Books Ltd, Chartwell House, London; 1996.
Google Scholar
Mertens T: Deadly & Dangerous Snakes. Magic Bean. Era Publications, Flinders Park, South Australia; 1995.
Google Scholar
Backshall S: Venomous Animals of the World. Johns Hopkins University Press, Maryland, USA; 2007.
Google Scholar
Stevens D: The Big Four Snakes: The Indian Cobra, the Common Krait, the Russell’s Viper, and the Saw-Scaled Viper. Webster’s Digital Services, USA; 2011.
Google Scholar
Premawardhena A, De Silva C, Fonseka M, Gunatilake S, De Silva H: Low dose subcutaneous adrenaline to prevent acute adverse reactions to antivenom serum in people bitten by snakes: randomised, placebo controlled trial. BMJ: Brit Med J 1999, 318(7190):1041. 10.1136/bmj.318.7190.1041
Article
Google Scholar
Warrell DA: The clinical management of snake bites in the Southeast Asian region. Southeast Asian J Trop Med Public Health 1999, 1(Suppl 1):1–89.
Google Scholar
Calvete JJ, Ju’arez P, Sanz L: Snake venomics. Strategy and applications. J Mass Spectrom 2007, 42(11):1405–1414. 10.1002/jms.1242
Article
Google Scholar
Sorower MS, Yeasin M: Robust Classification of Dialog Acts from the Transcription of Utterances. In ICSC 2007. IEEE International Conference on Semantic Computing, 3–10. 2007.
Google Scholar
Chanda P, Cho YR, Zhang A, Ramanathan M: Mining of attribute interactions using information theoretic metrics. In Data mining workshops, ICDMW’09. IEEE International Conference on Data Mining, Florida, USA; 2009:350–355.
Google Scholar
Devi MI, Rajaram R, Selvakuberan K: Generating best features for web page classification. Webology 5. 2008.
Google Scholar
Marquez-Vera C, Romero C: Ventura S: Predicting school failure using data mining. In Proceedings of the 4th International Conference on Educational Data Mining 271–276. 2011.
Google Scholar
John GH, Kohavi R, Pfleger K: Irrelevant features and the subset selection problem. In Proceedings of the eleventh international conference on machine learning, Volume 129, San Francisco 121–129. 1994.
Google Scholar
Jensen R, Shen Q: Fuzzy-rough sets assisted attribute selection. Fuzzy Systems, IEEE Transactions on 2007, 15: 73–89. 10.1109/TFUZZ.2006.889761
Article
Google Scholar
Meng YX: The practice on using machine learning for network anomaly intrusion detection. In IEEE International Conference on Machine Learning and Cybernetics (ICMLC), 2011, Vol. 2, 576–581. 2011.
Google Scholar
Indra Devi M, Rajaram R, Selvakuberan K: Automatic web page classification by combining feature selection techniques and lazy learners. In conference on computational intelligence and multimedia applications, 2007. Int Conference on 2007, 2: 33–37.
Google Scholar
Koonsanit K, Jaruskulchai C: Band selection for hyperspectral image using principal components anal-ysis and maxima-minima functional. In Knowledge, Information, and Creativity Support Systems. Thailand, Springer; 2011:103–112. 10.1007/978-3-642-24788-0_10
Chapter
Google Scholar
Frank E, Hall M, Holmes G, Kirkby R, Pfahringer B, Witten IH, Trigg L: Weka. In Data Mining and Knowledge Discovery Handbook. Springer, USA; 2005:1305–1314. 10.1007/0-387-25465-X_62
Chapter
Google Scholar
Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH: The WEKA data mining software: an update. ACM SIGKDD Explorations Newsletter 2009, 11: 10–18. 10.1145/1656274.1656278
Article
Google Scholar
James AP, Dimitrijev S: Ranked selection of nearest discriminating features. Hum-centric Comput Inform Sci 2012, 2: 12. 10.1186/2192-1962-2-12
Article
Google Scholar
Milacic M, James AP, Dimitrijev S: Biologically inspired features used for robust phoneme recognition. International Journal of Machine Intelligence and Sensory Signal Processing 2013, 1(1):46–54. 10.1504/IJMISSP.2013.052867
Article
Google Scholar
James AP, Maan AK: Improving feature selection algorithms using normalised feature histograms. Electron Lett 2011, 47(8):490–491. 10.1049/el.2010.3672
Article
Google Scholar
Longstaff ID, Cross JF: A pattern recognition approach to understanding the multi-layer perception. Pattern Recogn Lett 1987, 5(5):315–319. 10.1016/0167-8655(87)90072-9
Article
Google Scholar
Kim SB, Han KS, Rim HC, Myaeng SH: Some effective techniques for naive bayes text classification. Knowledge and Data Engineering, IEEE Transactions on 2006, 18(11):1457–1466. 10.1109/TKDE.2006.180
Article
Google Scholar
Freund Y, Schapire RE: A desicion-theoretic generalization of on-line learning and an application to boosting. In Computational learning theory, Springer 23–37. 1995.
Google Scholar
Benbouzid D, Busa-Fekete R, Casagrande N, Collin FD, Kégl B: MultiBoost: a multi-purpose boosting package. J Mach Learn Res 2012, 13: 549–553.
MATH
Google Scholar
Buhmann MD: Radial basis functions: theory and implementations, Volume 12. Cambridge university press. 2003.
Book
Google Scholar
Aha DW, Kibler D, Albert MK: Instance-based learning algorithms. Machine learning, Boston,USA; 1991.
Google Scholar
Atkeson CG, Moore AW, Schaal S: Locally weighted learning for control. Artif Intell Rev 1997, 11(1–5):75–113. 10.1023/A:1006511328852
Article
Google Scholar
Kohavi R: Bayes rule based and decision tree hybrid classifier. [US Patent 6,182,058]. 2001.
Google Scholar
Kotsiantis SB, Zaharakis ID, Pintelas PE: Machine learning: a review of classification and combining techniques. Artif Intell Rev 2006, 26(3):159–190. 10.1007/s10462-007-9052-3
Article
Google Scholar
Ho TK: The random subspace method for constructing decision forests. Pattern Anal Mach Intel, IEEE Transactions on 1998, 20(8):832–844.
Article
Google Scholar
Breiman L: Bagging predictors. Mach Learn 1996, 24(2):123–140.
MATH
MathSciNet
Google Scholar
Singhal A, Brown C: Dynamic Bayes net approach to multimodal sensor fusion. In Proceedings of the SPIE-The International Society for Optical Engineering, Volume 3209, 2–10. 1997.
Google Scholar