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检索条件"任意字段=7th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2016"

共 38 条记录，以下是1-10 订阅

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7th iapr tc3 workshop on artificial neural networks in pattern recognition, annpr 2016

7th IAPR TC3 Workshop on Artificial Neural Networks in Patte...

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7th iapr tc3 workshop on artificial neural networks in pattern recognition, annpr 2016

ISBN: (纸本)9783319461816

the proceedings contain 27 papers. the special focus in this conference is on Learning Algorithms, Architectures and Applications. the topics include: A spiking neural network for personalised modelling of electrogastrography;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;interpretable classifiers in precision medicine;on the evaluation of tensor-based representations for optimum-path forest 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;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;using radial basis function neural networks for continuous and discrete pain estimation from bio-physiological signals;emotion recognition in speech with deep learning architectures and on gestures and postural behavior as a modality in ensemble methods.

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A Spiking neural Network for Personalised Modelling of Electrogastrography (EGG) 7th

A Spiking Neural Network for Personalised Modelling of Elect...

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7th iapr tc3 workshop on artificial neural networks in pattern recognition (annpr)

作者： Breen, Vivienne Kasabov, Nikola Du, Peng Calder, Stefan Auckland Univ Technol Knowledge Engn & Discovery Res Inst Auckland New Zealand Univ Auckland Auckland Bioengn Inst Auckland New Zealand

ISBN: (纸本)9783319461823;9783319461816

EGG records the resultant body surface potential of gastric slow waves (electrical activity);while slow waves regulate contractions of gastric muscles, it is the electrical activity we are recording, not movement (like ECG records the cardiac electrical activity, but not the contractions of the heart, even the two are essentially related).

关键词： Spiking neural network Personalised modelling Electrogastrography Functional dyspepsia Slow wave dysrhythmia

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Comparing Incremental Learning Strategies for Convolutional neural networks 7th

Comparing Incremental Learning Strategies for Convolutional ...

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7th iapr tc3 workshop on artificial neural networks in pattern recognition (annpr)

作者： Lomonaco, Vincenzo Maltoni, Davide Univ Bologna DISI Bologna Italy

ISBN: (纸本)9783319461823;9783319461816

In the last decade, Convolutional neural networks (CNNs) have shown to perform incredibly well in many computer vision tasks such as object recognition and object detection, being able to extract meaningful high-level invariant features. However, partly because of their complex training and tricky hyper-parameters tuning, CNNs have been scarcely studied in the context of incremental learning where data are available in consecutive batches and retraining the model from scratch is unfeasible. In this work we compare different incremental learning strategies for CNN based architectures, targeting real-word applications.

关键词： Deep learning Incremental learning Convolutional neural networks

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Using Radial Basis Function neural networks for Continuous and Discrete Pain Estimation from Bio-physiological Signals 7th

Using Radial Basis Function Neural Networks for Continuous a...

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7th iapr tc3 workshop on artificial neural networks in pattern recognition (annpr)

作者： Amirian, Mohammadreza Kaechele, Markus Schwenker, Friedhelm Univ Ulm Inst Neural Informat Proc Ulm Germany

ISBN: (纸本)9783319461823;9783319461816

In this work we present extensions for Radial Basis Function networks to improve their ability for discrete and continuous pain intensity estimation. Besides proposing a mid-level fusion scheme, the use of standardization and unconventional loss functions are covered. We show that RBF networks can be improved in this way and present extensive experimental validation to support our findings on a multi-modal dataset.

关键词： Radial basis function networks

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Predictive Segmentation Using Multichannel neural networks in Arabic OCR System 7th

Predictive Segmentation Using Multichannel Neural Networks i...

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7th iapr tc3 workshop on artificial neural networks in pattern recognition (annpr)

作者： Radwan, Mohamed A. Khalil, Mahmoud I. Abbas, Hazem M. Ain Shams Univ Fac Engn Comp & Syst Engn Dept Cairo Egypt

ISBN: (纸本)9783319461823;9783319461816

this article offers an open vocabulary Arabic text recognition system using two neural networks, one for segmentation and another one for characters recognition. the problem of words segmentation in Arabic language, like many cursive languages, presents a challenge to the OCR systems. this paper presents a multichannel neural network to solve offline segmentation of machine-printed Arabic documents. the segmented characters are then used as input to a convolutional neural network for Arabic characters recognition. the accuracy of the segmentation model using one font is 98.9%, while four-font model showed 95.5% accuracy. the accuracy of characters recognition on Arabic Transparent font of size 18 pt from APTI data set is 94.8%.

关键词： Arabic segmentation OCR Convolutional neural networks

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Soft-Constrained Nonparametric Density Estimation with artificial neural networks 7th

Soft-Constrained Nonparametric Density Estimation with Artif...

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7th iapr tc3 workshop on artificial neural networks in pattern recognition (annpr)

作者： Trentin, Edmondo Univ Siena Dipartimento Ingn Informaz & Sci Matemat Siena Italy

ISBN: (纸本)9783319461823;9783319461816

the estimation of probability density functions (pdf) from unlabeled data samples is a relevant (and, still open) issue in pattern recognition and machine learning. Statistical parametric and nonparametric approaches present severe drawbacks. Only a few instances of neural networks for pdf estimation are found in the literature, due to the intrinsic difficulty of unsupervised learning under the necessary integral-equals-one constraint. In turn, also such neural networks do suffer from serious limitations. the paper introduces a soft-constrained algorithm for training a multilayer perceptron (MLP) to estimate pdfs empirically. A variant of the Metropolis-Hastings algorithm (exploiting the very probabilistic nature of the MLP) is used to satisfy numerically the constraint on the integral of the function learned by the MLP. the preliminary outcomes of a simulation on data drawn from a mixture of Fisher-Tippett pdfs are reported on, and compared graphically with the estimates yielded by statistical techniques, showing the viability of the approach.

关键词： Density estimation Nonparametric estimation Unsupervised learning Constrained learning Multilayer perceptron

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Emotion recognition in Speech with Deep Learning Architectures 7th

Emotion Recognition in Speech with Deep Learning Architectur...

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7th iapr tc3 workshop on artificial neural networks in pattern recognition (annpr)

作者： Erdal, Mehmet Kaechele, Markus Schwenker, Friedhelm Univ Ulm Inst Neural Informat Proc D-89081 Ulm Germany

ISBN: (纸本)9783319461823;9783319461816

Deep neural networks (DNNs) became very popular for learning abstract high-level representations from raw data. this lead to improvements in several classification tasks including emotion recognition in speech. Besides the use as feature learner a DNN can also be used as classifier. In any case it is a challenge to determine the number of hidden layers and neurons in each layer for such networks. In this work the architecture of a DNN is determined by a restricted grid-search with the aim to recognize emotion in human speech. Because speech signals are essentially time series the data will be transformed in an appropriate format to use it as input for deep feed forward neural networks without losing much time dependent information. Furthermore the Elman-Net will be examined. the results shows that by maintaining time dependent information in the data better classification accuracies can be achieved with deep architectures.

关键词： Emotion recognition

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Towards Effective Classification of Imbalanced Data with Convolutional neural networks 7th

Towards Effective Classification of Imbalanced Data with Con...

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7th iapr tc3 workshop on artificial neural networks in pattern recognition (annpr)

作者： Raj, Vidwath Magg, Sven Wermter, Stefan Univ Hamburg Dept Informat Knowledge Technol Hamburg Germany

ISBN: (纸本)9783319461823;9783319461816

Class imbalance in machine learning is a problem often found with real-world data, where data from one class clearly dominates the dataset. Most neural network classifiers fail to learn to classify such datasets correctly if class-to-class separability is poor due to a strong bias towards the majority class. In this paper we present an algorithmic solution, integrating different methods into a novel approach using a class-to-class separability score, to increase performance on poorly separable, imbalanced datasets using Cost Sensitive neural networks. We compare different cost functions and methods that can be used for training Convolutional neural networks on a highly imbalanced dataset of multi-channel time series data. Results show that, despite being imbalanced and poorly separable, performance metrics such as G-Mean as high as 92.8% could be reached by using cost sensitive Convolutional neural networks to detect patterns and correctly classify time series from 3 different datasets.

关键词： Cost functions

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On Gestures and Postural Behavior as a Modality in Ensemble Methods 7th

On Gestures and Postural Behavior as a Modality in Ensemble ...

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7th iapr tc3 workshop on artificial neural networks in pattern recognition (annpr)

作者： Hihn, Heinke Meudt, Sascha Schwenker, Friedhelm Univ Ulm Inst Neural Informat Proc D-89069 Ulm Germany

ISBN: (纸本)9783319461823;9783319461816

Knowledge about the users emotional state is important to achieve human like, natural HCI in modern technical systems. Humans rely on body gestures and posture when communicating. We investigate the relation between gestures and human emotion, specifically when completing tasks. the main focus of this work lies on discriminating between mental overload and mental underload, which can e.g. be useful in an e-tutorial system. Mental underload is a new term used to describe the state a person is in when completing a dull or boring task. It will be shown how to select suited features, such as gestures, movement and postural behavior. Furthermore those features will be investigated regarding their discriminative power. After features are selected, a multiple classifier system will be designed, trained and evaluated.

关键词： artificial intelligence

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On the Evaluation of Tensor-Based Representations for Optimum-Path Forest Classification 7th

On the Evaluation of Tensor-Based Representations for Optimu...

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7th iapr tc3 workshop on artificial neural networks in pattern recognition (annpr)

作者： Lopes, Ricardo Costa, Kelton Papa, Joao Inst Pesquisas Eldorado Campinas SP Brazil Sao Paulo State Univ Dept Comp Sao Paulo Brazil

ISBN: (纸本)9783319461823;9783319461816

Tensor-based representations have been widely pursued in the last years due to the increasing number of high-dimensional datasets, which might be better described by the multilinear algebra. In this paper, we introduced a recent pattern recognition technique called Optimum-Path Forest (OPF) in the context of tensor-oriented applications, as well as we evaluated its robustness to space transformations using Multilinear Principal Component Analysis in both face and human action recognition tasks considering image and video datasets. We have shown OPF can obtain more accurate recognition rates in some situations when working on tensor-oriented feature spaces.

关键词： Optimum-Path Forest Tensors Gait and face recognition

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