Search Results

11 - 20 of 54 items :

  • Applied Mathematics x
Clear All
Times Series Averaging and Denoising from a Probabilistic Perspective on Time–Elastic Kernels

Recognition, ICPR 2008, Tampa, FL, USA , pp. 1–4. Juang, B. (1985). On the hidden Markov model and dynamic time warping for speech recognition—A unified view, AT&T Bell Laboratories Technical Journal 63 (7): 1213–1242. Just, W. and Just, W. (1999). Computational complexity of multiple sequence alignment with SP-score, Journal of Computational Biology 8 (6): 615–623. Kaiser, R. and Knight, W. (1979). Digital signal averaging, Journal of Magnetic Resonance (1969) 36 (2): 215–220. Keogh, E.J., Xi, X., Wei, L. and Ratanamahatana, C. (2006). The

Open access
Note onset detection in musical signals via neural–network–based multi–ODF fusion

Introduction to Audio Content Analysis: Applications in Signal Processing and Music Informatics , Wiley/IEEE Press, Hoboken, NJ. MIREX (2013). Audio onset detection results in Music Information Retrieval Evaluation eXchange MIREX, 2013, http://nema.lis.illinois.edu/nema_out/mirex2013/results/aod/summary.html . Peeters, G. (2005). Time variable tempo detection and beat marking, Proceedings of the International Computer Music Conference, ICMC 2005, Barcelona, Spain , pp. 1–4. Quintela, N.D., Giménez, A.P. and Guijarro, S.T. (2009). A comparison of score

Open access
A dynamic model of classifier competence based on the local fuzzy confusion matrix and the random reference classifier

. (1979). A simple sequentially rejective multiple test procedure, Scandinavian Journal of Statistics 6 (2): 65–70. Hsieh, N.-C. and Hung, L.-P. (2010). A data driven ensemble classifier for credit scoring analysis, Expert systems with Applications 37 (1): 534–545. Huenupán, F., Yoma, N.B., Molina, C. and Garretón, C. (2008). Confidence based multiple classifier fusion in speaker verification, Pattern Recognition Letters 29 (7): 957–966. Jurek, A., Bi, Y., Wu, S. and Nugent, C. (2013). A survey of commonly used ensemble-based classification

Open access
A practical application of kernel-based fuzzy discriminant analysis

-6419. Gao, Q.X., Zhang, L. and Zhang, D. (2008). Face recognition using FLDA with single training image per person, Applied Mathematics and Computation 205(2): 726-734. Hastie, T., Buja, A. and Tibshirani, R. (1995). Penalized discriminant analysis, The Annals of Statistics 23(1): 73-102. Hastie, T., Tibshirani, R. and Buja, A. (1994). Flexible discriminant analysis by optimal scoring, Journal of the American Statistical Association 89(428): 1255-1270. Hastie, T., Tibshirani, R., Friedman, J. and Franklin, J. (1991). The

Open access
A new lightweight method for security risk assessment based on fuzzy cognitive maps

. 191-198. Hoo, K.J.S. (2000). How much is enough? A risk-management approach to computer security, Working Paper , Stanford University, Stanford, CA, pp. 1-99. Hubbard, D. and Evans, D. (2010). Problems with scoring methods and ordinal scales in risk assessment, Journal of Research and Development 54 (3): 1-10. Institute for Computer Sciences and Technology (1979). Guideline for Automatic Data Processing Risk Analysis , National Bureau of Standards, Washington, DC. ISO/IEC (2011). Information

Open access
Random Projection RBF Nets for Multidimensional Density Estimation

Harris C. J. (2004). Sparse kernel density construction using orthogonal forward regression with leave-one-out test score and local regularization, IEEE Transactions on Systems, Man, and Cybernetics, Part B , 34(4): 1708-1717. Dasgupta S. and Gupta A. (2003). An elementary proof of a theorem of Johnson and Lindenstrauss, Random Structures and Algorithms 22(1): 60-65. Devroye L. and Györfi L. (1985). Nonparametric Density Estimation. The L 1 View. Wiley, New York, NY. Devroye L., Györfi

Open access
Intrachromosomal regulation decay in breast cancer

regimes among chromosomes in breast cancer transcriptomes. Fig. 2 Model validation. A heatmap depicting Z-scored relative likelihood – as measured by adjusted coefficient of determination R2 – for the four models. Rows correspond to chromosomes, while columns correspond to the four models for nonscaled distance. Green tones correspond to low-to-negative scores, while the dark and red colours represent positive-to-high scores. 3.1 Decay of gene–gene correlations The model discrimination analysis we performed indicates that the best goodness of fit (by

Open access
eppex: Epochal Phrase Table Extraction for Statistical Machine Translation

-2031 Goyal, Amit, Hal Daumé, III, and Suresh Venkatasubramanian. Streaming for large scale NLP: language modeling. In Proc. of HTL/NAACL , pages 512-520, Boulder, Colorado, 2009. URL http://portal.acm.org/citation.cfm?id=1620754.1620829 http://portal.acm.org/citation.cfm?id=1620754.1620829 Hardmeier, Christian. Fast and Extensible Phrase Scoring for Statistical Machine Translation. The Prague Bulletin of Mathematical Linguistics , 93:79-88, 2010. Johnson, J Howard, Joel Martin, George Foster, and Roland Kuhn. Improving

Open access
rgbF: An Open Source Tool for n-gram Based Automatic Evaluation of Machine Translation Output

rgbF: An Open Source Tool for n-gram Based Automatic Evaluation of Machine Translation Output

We describe RGBF, a tool for automatic evaluation of machine translation output based on n-gram precision and recall. The tool calculates the F-score averaged on all n-grams of an arbitrary set of distinct units such as words, morphemes, POS tags, etc. The arithmetic mean is used for n-gram averaging. As input, the tool requires reference translation(s) and hypothesis, both containing the same combination of units. The default output is the document level 4-gram F-score of the desired unit combination. The scores at the sentence level can be obtained on demand, as well as precision and/or recall scores, separate unit scores and separate n-gram scores. In addition, weights can be introduced both for n-grams and for units, as well as the desired n-gram order n.

Open access
Affine Transformation Based Ontology Sparse Vector Learning Algorithm

al. [ 8 ], Agapito et al. [ 9 ], Umadevi et al. [ 10 ] and Cohen [ 11 ]). The model of ontology can be regarded as a graph G = ( V , E ), in which each vertex v expresses a concept and each edge e = v i v j represents a directly contact between two concepts v i and v j . The aim of ontology similarity calculating is to learn a similarity function Sim : V × V → ℝ + ∪ {0} which maps each pair of vertices to a score real number. Moreover, the purpose of ontology mapping is to bridge the link between two or more different ontologies based on the

Open access