nach oben

Soft Computing

Erschienen in:

17.10.2014 | Methodologies and Application

VC-based confidence and credibility for support vector machines

verfasst von: George E. Sakr, Imad H. Elhajj

Erschienen in: Soft Computing | Ausgabe 1/2016

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Assigning a confidence and a credibility measures is a challenging stochastic inference problem. Some algorithms only yield the predicted value without evaluating the measure of confidence or credibility over the decision. Support vector machines (SVM) is one algorithm that showed state-of-the-art decision accuracy but lacks a measure of confidence and credibility over the decisions. In this paper we propose a new confidence measure based on the Vapnik and Chervonenkis (VC) dimension of a learning algorithm and the notion of complexity as defined by Kolmogorov. We also propose a new credibility measure based on the VC dimension. The resulting confidence and credibility measures are then tested on the well-known US postal handwritten digit recognition, on the Wisconsin breast cancer dataset and are also tested for agitation detection. The results show high and improved correlation between the decision and the confidence/credibility measures compared to Vovk’s and Platt’s methods.

Vorheriger Artikel Involutive right-residuated l-groupoids

Nächster Artikel Combustion optimization of a coal-fired boiler with double linear fast learning network

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Aronszajn N (1950) Introduction to the theory of Hilbert spaces. Reasearch [sic] Foundation, StillwaterMATH

Brier G (1950) Verification of forecasts expressed in terms of probability. Mon Weather Rev 78(1):1–3CrossRef

Burges C (1998) A tutorial on support vector machines for pattern recognition. Data Mining Knowl Discov 2(2):121–167CrossRef

Cun Y, Boser B, Denker J, Howard R, Habbard W, Jackel L, Henderson D (1990) Handwritten digit recognition with a back-propagation network. In: Advances in neural information processing systems 2, Morgan Kaufmann Publishers Inc. pp 396–404

Dawid A (1983) Inference, statistical: I. Encycl Stat Sci 4:89–105

Dumais S, Platt J, Heckerman D, Sahami M (1998) Inductive learning algorithms and representations for text categorization. In: Proceedings of the seventh international conference on Information and knowledge management, ACM. pp 148–155

Ercan, Secil, Kayakutlu G (2014) Patent value analysis using support vector machines. In: Soft computing, vol 18. Springer, pp 313–328

Gammerman A, Vapnik V., Vowk V (1998) Learning by transduction. In: Uncertainty in Artificial Intelligence, Morgan Kaufmann. pp 148–155

Joachims T (1998) Text categorization with support vector machines: Learning with many relevant features. Machine Learning: ECML-98. pp 137–142

Kecman V (2001) Learning and soft computing: support vector machines, neural networks, and fuzzy logic models. The MIT press, London

Kolmogorov A (1968) Three approaches to the quantitative definition of information. Int J Comput Math 2(1):157–168MATHMathSciNetCrossRef

Li L, Chen J (2006) Emotion recognition using physiological signals. Advances in Artificial Reality and Tele-Existence. pp 437–446

Liu, Zhi and Xu, Shuqiong and Zhang, Yun and Chen, Xin and Chen, Philip CL (2013) Interval type-2 fuzzy kernel based support vector machine algorithm for scene classification of humanoid robot. In: Soft Computing, Springer, pp 1–18

Melluish T, Saunders C, Nouretdinov I, Vovk V (2001) The typicalness framework: a comparison with the bayesian approach. University of London, Royal Holloway

Mitra P, Murthy C, Pal S (2004) A probabilistic active support vector learning algorithm. IEEE Trans Pattern Anal Mach Intell 26(3):413–418CrossRef

Platt J (1999) Probabilities for SV machines. Advances in Neural Information Processing Systems. pp 61–74

Sakr G, Elhajj I, Huijer H (2010) Support Vector Machines to Define and Detect Agitation Transition. IEEE Transactions on Affective Computing

Sakr G, Elhajj I, Huijer H, Riley-Doucet C, Debnath D (2008) Subject independent agitation detection. In: IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2008. pp 200–204

Sakr G, Elhajj I, Wejinya U (2009) multi level SVM for subject independent agitation detection. In: IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2009, pp 538–543

Schölkopf B, Smola A (2002) Learning with kernels. The MIT Press

Spielberger CD, Gorsuch RL, Edward LR (1970) STAI manual for the State-Trait Anxiety Inventory (“self-evaluation questionnaire”). In: Consulting Psychologists Press

Street N, Wolberg W, Mangasarian O (1992) Nuclear feature extraction for breast tumor diagnosis. University of Wisconsin-Madison, Computer Sciences Department

Stroop JR (1935) Studies of interference in serial verbal reactions. J Exp Psychol 18:643–662CrossRef

Vapnik V (1995) The nature of statistical learnin. Springer, New YorkCrossRef

Vapnik V (1998) Statistical learning theory. Wiley, New YorkMATH

Vovk V, Gammerman A, Saunders C (1999) Machine-learning applications of algorithmic randomness. In: Proceedings of the Sixteenth International Conference on Machine Learning, Morgan Kaufmann Publishers Inc. pp 444–453

Titel: VC-based confidence and credibility for support vector machines
verfasst von: George E. Sakr
Imad H. Elhajj
Publikationsdatum: 17.10.2014
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 1/2016
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-014-1485-4

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 1/2016

Possibilistic AIRS induction from uncertain data

Studying the multiobjective variable neighbourhood search algorithm when solving the relay node placement problem in Wireless Sensor Networks

Healthy diet assessment mechanism based on fuzzy markup language for Japanese food

On interval-valued hesitant fuzzy rough approximation operators

Combustion optimization of a coal-fired boiler with double linear fast learning network

Computational modelling of interruptional activities between transposable elements using grammars and the linear ordering problem