By Gabriel B. P. Costa, Moacir Ponti, Alejandro C. Frery (auth.), Zhi-Hua Zhou, Friedhelm Schwenker (eds.)
ISBN-10: 3642407048
ISBN-13: 9783642407048
ISBN-10: 3642407056
ISBN-13: 9783642407055
This booklet constitutes the completely refereed revised chosen papers from the second one IAPR foreign Workshop, PSL 2013, held in Nanjing, China, in may possibly 2013. the ten papers incorporated during this quantity have been conscientiously reviewed and chosen from 26 submissions. in part supervised studying is a swiftly evolving region of computing device studying. It generalizes many types of studying paradigms together with supervised and unsupervised studying, semi-supervised studying for class and regression, transductive studying, semi-supervised clustering, multi-instance studying, susceptible label studying, coverage studying in in part observable environments, etc.
Read Online or Download Partially Supervised Learning: Second IAPR International Workshop, PSL 2013, Nanjing, China, May 13-14, 2013, Revised Selected Papers PDF
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Additional resources for Partially Supervised Learning: Second IAPR International Workshop, PSL 2013, Nanjing, China, May 13-14, 2013, Revised Selected Papers
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Biomed. Eng. 46(7), 281–289 (2007) 25. : A circumplex model of affect. J. Pers. Soc. Psychol. 39(6), 1161–1178 (1980) 26. : The interaction of cognitive and physiological determinants of emotional state. In: Berkowitz, L. ) Advances in Experimental Social Psychology, vol. 1, pp. 49–80+. Academic Press, New York (1964) 27. : Classification of labeled and unlabeled bio-physiological data. , Trentin, E. ) PSL 2011. LNCS, vol. 7081, pp. 138–147. Springer, Heidelberg (2012) 28. : Multi-modal classifier-fusion for the classification of emotional states in WOZ scenarios.
The training- and test-datasets have to be projected in the space ... dictionary learning ... (a) (b) (c) Fig. 3. In this figure, the workflow of the proposed algorithm is depicted. The upper part shows the input signal and the segmentation into smaller overlapping sequences. The segmentation takes the position of a peak as reference value and extracts a window of predetermined length around it. The results of this step can be seen in column (a). To correct for possible inaccuracies, a small window around the peak location is used to determine the optimal shift of the sequence to maximize a correlation value with a reference peak (column (b)).
The training set size was gradually increased from 100 to 9300. The sparsity value was set to 3 and the dictionary dimension (number of atoms) was set to 60. In this experiment, the differentiation between the individual days was released thus allowing the training and test sets to contain data from every daily measurement. The validation of the classification result was done by cross validation with 20 % of the samples left out and the results are averaged over 30 trials. 2 Effects of Varying the Sparsity Value The high degree of sparseness of the representations that can be achieved with dictionary learning renders it very useful for specific tasks.
Partially Supervised Learning: Second IAPR International Workshop, PSL 2013, Nanjing, China, May 13-14, 2013, Revised Selected Papers by Gabriel B. P. Costa, Moacir Ponti, Alejandro C. Frery (auth.), Zhi-Hua Zhou, Friedhelm Schwenker (eds.)
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