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Artificial neural networks in pattern recognition = 7th IAPR TC3 Workshop, ANNPR 2016, Ulm, Germany, September 28-30, 2016 : proceedings /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Artificial neural networks in pattern recognition/ edited by Friedhelm Schwenker ... [et al.].
Reminder of title:	7th IAPR TC3 Workshop, ANNPR 2016, Ulm, Germany, September 28-30, 2016 : proceedings /
remainder title:	ANNPR 2016
other author:	Schwenker, Friedhelm.
corporate name:	Workshop on the Preservation of Stability under Discretization
Published:	Cham :Springer International Publishing : : 2016.,
Description:	xi, 335 p. :ill., digital ; : 24 cm.;
Contained By:	Springer eBooks
Subject:	Neural networks (Computer science) -
Online resource:	http://dx.doi.org/10.1007/978-3-319-46182-3
ISBN:	9783319461823

Artificial neural networks in pattern recognition = 7th IAPR TC3 Workshop, ANNPR 2016, Ulm, Germany, September 28-30, 2016 : proceedings /
Artificial neural networks in pattern recognition7th IAPR TC3 Workshop, ANNPR 2016, Ulm, Germany, September 28-30, 2016 : proceedings /[electronic resource] :ANNPR 2016edited by Friedhelm Schwenker ... [et al.]. - Cham :Springer International Publishing :2016. - xi, 335 p. :ill., digital ;24 cm. - Lecture notes in computer science,98960302-9743 ;. - Lecture notes in computer science ;6140..

Learning sequential data with the help of linear systems -- A spiking neural network for personalised modelling of Electrogastogrophy (EGG) -- Improving generalization abilities of maximal average margin classifiers -- Finding small sets of random Fourier features for shift-invariant kernel approximation -- Incremental construction of low-dimensional data representations -- Soft-constrained nonparametric density estimation with artificial neural networks -- Density based clustering via dominant sets -- Co-training with credal models -- Interpretable classifiers in precision medicine: feature selection and multi-class categorization -- On the evaluation of tensor-based representations for optimum-pathforest classification -- On the harmony search using quaternions -- Learning parameters in deep belief networks through firefly algorithm -- Towards effective classification of imbalanced data with convolutional neural networks -- On CPU performance optimization of restricted Boltzmann machine and convolutional RBM -- Comparing incremental learning strategies for convolutional neural networks -- Approximation of graph edit distance by means of a utility matrix -- Time series classification in reservoir- and model-space: a comparison -- Objectness scoring and detection proposals in forward-Looking sonar images with convolutional neural networks -- Background categorization for automatic animal detection in aerial videos using neural networks -- Predictive segmentation using multichannel neural networks in Arabic OCR system -- Quad-tree based image segmentation and feature extraction to recognize online handwritten Bangla characters -- A hybrid recurrent neural network/dynamic probabilistic graphical model predictor of the disulfide bonding state of cysteines from the primary structure of proteins -- Using radial basis function neural networks for continuous anddiscrete pain estimation from bio-physiological signals -- Active learning for speech event detection in HCI -- Emotion recognition in speech with deep learning architectures -- On gestures and postural behavior as a modality in ensemble methods -- Machine learning driven heart rate detection with camera photoplethysmography in time domain.

This book constitutes the refereed proceedings of the 7th IAPR TC3 International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2016, held in Ulm, Germany, in September 2016. The 25 revised full papers presented together with 2 invited papers were carefully reviewed and selected from 32 submissions for inclusion in this volume. The workshop will act as a major forum for international researchers and practitioners working in all areas of neural network- and machine learning-based pattern recognition to present and discuss the latest research, results, and ideas in these areas.

ISBN: 9783319461823

Standard No.: 10.1007/978-3-319-46182-3doiSubjects--Topical Terms:

528588
Neural networks (Computer science)

LC Class. No.: TK7882.P3 / A56 2016

Dewey Class. No.: 006.32

Artificial neural networks in pattern recognition = 7th IAPR TC3 Workshop, ANNPR 2016, Ulm, Germany, September 28-30, 2016 : proceedings /
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