Fast Parameterless Ballistic Launch Point Estimation based on k-NN Search

This paper discusses the problem of estimating a ballistic trajectory and the launch point by using a trajectory similarity search in a database. The major difficulty of this problem is that estimation accuracy is guaranteed only when an identical trajectory exists in the trajectory database (TD). Hence, the TD must comprise an impractically great number of trajectories from various launch points. Authors proposed a simplified trajectory database with a single launch point and a trajectory similarity search algorithm that decomposes trajectory similarity into velocity and position components. These similarities are applied k-NN estimation. Furthermore, they used the iDistance technique to partition the data space of the high-dimensional database for an efficient k-NN search. Authors proved the effectiveness of the proposed algorithm by experiment.Defence Science Journal, Vol. 64, No. 1, January 2014, DOI:10.14429/dsj.64.295

Neighborhood Property based Pattern Selection For Support Vector Machines

Support Vector Machine (SVM) has been spotlighted in the machine learning community thanks to its theoretical soundness and practical performance. When applied to a large data set, however, it requires a large memory and long time for training. To cope with the practical difficulty, we propose a pattern selection algorithm based on neighborhood properties. The idea is to select only the patterns that are likely to be located near the decision boundary. Those patterns are expected to be more informative than the randomly selected patterns. The experimental results provide promising evidence that it is possible to successfully employ the proposed algorithm ahead of SVM training. 1

A hybrid novelty score and its use in keystroke dynamics-based user authentication

The purpose of novelty detection is to detect (novel) patterns that are not generated by the identical distribution of the normal class. A distance-based novelty detector classifies a new data pattern as "novel" if its distance from "normal" patterns is large. It is intuitive, easy to implement, and fits naturally With incremental learning. Its performance is limited, however, because it relies only on distance. In this paper, we propose considering topological relations as well. We compare our proposed method with 13 other novelty detectors based on 21 benchmark data sets from two sources. We then apply our method to a real-world application in which incremental learning is necessary: keystroke dynamics-based user authentication. The experimental results are promising. Not only does our method improve the performance of distance-based novelty detectors, but it also outperforms the other non-distance-based algorithms. Our method also allows efficient model updates. (C) 2009 Elsevier Ltd. All rights reserved.Juszczak P, 2009, NEUROCOMPUTING, V72, P1859, DOI 10.1016/j.neucom.2008.05.003Kang P, 2008, PATTERN RECOGN, V41, P3507, DOI 10.1016/j.patcog.2008.04.009Kang P, 2008, COMPUT SECUR, V27, P3, DOI 10.1016/j.cose.2008.02.001Lee HJ, 2008, ARTIF INTELL MED, V42, P199, DOI 10.1016/j.artmed.2007.11.001Lee HJ, 2007, COMPUT SECUR, V26, P300, DOI 10.1016/j.cose.2006.11.006Harmeling S, 2006, NEUROCOMPUTING, V69, P1608, DOI 10.1016/j.neucom.2005.05.015Cherry GA, 2006, IEEE T SEMICONDUCT M, V19, P159, DOI 10.1109/TSM.2006.873524Nanni L, 2006, NEUROCOMPUTING, V69, P869, DOI 10.1016/j.neucom.2005.06.007Yu EZ, 2006, EXPERT SYST APPL, V30, P352, DOI 10.1016/j.eswa.2005.07.026BOUTSINAS B, 2006, PATTERN RECOGN, V16, P143, DOI 10.1134/S1054661806020015Clarke NL, 2005, COMPUT SECUR, V24, P519, DOI 10.1016/j.cose.2005.08.003Barreto GA, 2005, IEEE T NEURAL NETWOR, V16, P1064, DOI 10.1109/TNN.2005.853416Araujo LCF, 2005, IEEE T SIGNAL PROCES, V53, P851, DOI 10.1109/TSP.2004.839903Angiulli F, 2005, IEEE T KNOWL DATA EN, V17, P203, DOI 10.1109/TKDE.2005.31SARMIENTO T, 2005, P 2005 IEEE SEMI ADV, P139FURNELL S, 2005, COMPUT FRAUD SEC AUG, P9Yan J, 2004, IEEE SECUR PRIV, V2, P25, DOI 10.1109/MSP.2004.81Peacock A, 2004, IEEE SECUR PRIV, V2, P40, DOI 10.1109/MSP.2004.89PRABHAKAR S, 2003, IEEE SECUR PRIV, V1, P33, DOI 10.1109/MSECP.2003.1193209JOLLIFFE I, 2003, PRINCIPLE COMPONENTYegnanarayana B, 2002, NEURAL NETWORKS, V15, P459, DOI 10.1016/S0893-6080(02)00019-9MONROSE F, 2002, INT J INFORMATION SE, V1, P69, DOI 10.1007/s102070100006Scholkopf B, 2001, NEURAL COMPUT, V13, P1443Ratsch G, 2001, MACH LEARN, V42, P287TAX D, 2001, THESIS DELFT U TECHNPizzi NJ, 2001, ARTIF INTELL MED, V21, P263Guha S, 2000, INFORM SYST, V25, P345Knorr EM, 2000, VLDB J, V8, P237Monrose F, 2000, FUTURE GENER COMP SY, V16, P351MCLACHLAN G, 2000, FINITE MIXTURE MODELDUDA RO, 2000, PATTERN CLASSIFICATIRAMASWAMY S, 2000, P INT C MAN DAT SIGMKARYPIS G, 1999, IEEE COMPUT, V32, P68, DOI DOI 10.1109/2.781637JAIN AK, 1999, BIOMETRICS PERSONALScholkopf B, 1998, NEURAL COMPUT, V10, P1299GUHA S, 1998, P ACM SIGMOD INT C M, P73PLATT JC, 1998, ADV KERNEL METHODS S, P41YPMA A, 1998, P INT C ART NEUR NETOSUNA E, 1997, P 1997 IEEE WORKSH N, P276BURGE P, 1997, P AI APPR FRAUD DET, P9POLEMI D, 1997, BIOMETRIC TECHNIQUESZHANG T, 1996, P 1996 ACM SIGMOD IN, P103, DOI DOI 10.1145/235968.233324BISHOP CM, 1995, NEURAL NETWORK PATTEBARNETT V, 1994, OUTLIERS STAT DATAVAPNIK VN, 1982, ESTIMATION DEPENDENCHAWKINS DM, 1980, IDENTIFICATION OUTLI