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共 274 条记录，以下是91-100 订阅

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Manifold Learning Regression with Non-stationary Kernels 8th

Manifold Learning Regression with Non-stationary Kernels

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8th iapr TC3 workshop on artificial neural networks in pattern recognition (ANNPR)

作者： Kuleshov, Alexander Bernstein, Alexander Burnaev, Evgeny Skolkovo Inst Sci & Technol Skolkovo Innovat Ctr 3 Nobel St Moscow 121205 Russia

ISBN: (纸本)9783319999784;9783319999777

Nonlinear multi-output regression problem is to construct a predictive function which estimates an unknown smooth mapping from q-dimensional inputs to m-dimensional outputs based on a training data set consisting of given "input-output" pairs. In order to solve this problem, regression models based on stationary kernels are often used. However, such approaches are not efficient for functions with strongly varying gradients. There exist some attempts to introduce non-stationary kernels to account for possible non-regularities, although even the most efficient one called Manifold Learning Regression (MLR), which estimates the unknown function as well its Jacobian matrix, is too computationally expensive. The main problem is that the MLR is based on a computationally intensive manifold learning technique. In this paper we propose a modified version of the MLR with significantly less computational complexity while preserving its accuracy.

关键词： Nonlinear multi-output regression Manifold learning regression Non-stationary kernel

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Selecting Features from Foreign Classes 8th

Selecting Features from Foreign Classes

引用

8th iapr TC3 workshop on artificial neural networks in pattern recognition (ANNPR)

作者： Lausser, Ludwig Szekely, Robin Kessler, Viktor Schwenker, Friedhelm Kestler, Hans A. Ulm Univ Inst Med Syst Biol D-89069 Ulm Germany Ulm Univ Inst Neural Informat Proc D-89069 Ulm Germany

ISBN: (纸本)9783319999784;9783319999777

Supervised learning algorithms restrict the training of classification models to the classes of interest. Other related classes are typically neglected in this process and are not involved in the final decision rule. Nevertheless, the analysis of these foreign samples and their labels might provide additional information on the classes of interest. By revealing common patterns in foreign classification tasks it might lead to the identification of structures suitable for the original classes. This principle is used in the field of transfer learning. In this work, we investigate the use of foreign classes for the feature selection process of binary classifiers. While the final classification model is trained according to the traditional supervised learning scheme, its feature signature is designed for separating a pair of foreign classes. We systematically analyse these classifiers in 10x10 cross-validation experiments on microarray datasets with multiple diagnostic classes. For each evaluated classification model, we observed foreign feature combinations that outperformed at least 90% of those feature sets designed for the original diagnostic classes on at least 88.9% of all datasets.

关键词： Supervised learning

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A κ-nearest neighbor based algorithm for multi-instance multi-label active learning 8th

A κ-nearest neighbor based algorithm for multi-instance mul...

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8th iapr TC3 workshop on artificial neural networks for pattern recognition, ANNPR 2018

作者： Ruiz, Adrian T. Thiam, Patrick Schwenker, Friedhelm Palm, Günther Institute of Neural Information Processing Ulm University James-Franck-Ring Ulm89081 Germany

ISBN: (纸本)9783319999777

Multi-instance multi-label learning (MIML) is a framework in machine learning in which each object is represented by multiple instances and associated with multiple labels. This relatively new approach has achieved success in various applications, particularly those involving learning from complex objects. Because of the complexity of MIML, the cost of data labeling increases drastically along with the improvement of the model performance. In this paper, we introduce a MIML active learning approach to reduce the labeling costs of MIML data without compromising the model performance. Based on a query strategy, we select and request from the Oracle the label set of the most informative object. Our approach is formulated in a pool-based scenario and uses Miml-κ nn as the base classifier. This classifier for MIML is based on the κ -Nearest Neighbor algorithm and has achieved superior performance in different data domains. We proposed novel query strategies and also implemented previously used query strategies for MIML learning. Finally, we conducted an experimental evaluation on various benchmark datasets. We demonstrate that these approaches can achieve significantly improved results than without active selection for all datasets on various evaluation criteria. © Springer Nature Switzerland AG 2018.

关键词： Text processing

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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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Audio Visual Speech recognition Using Deep Recurrent neural networks 1

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4th iapr TC 9 workshop on pattern recognition of Social Signals in Human-Computer-Interaction (MPRSS)

作者： Thanda, Abhinav Venkatesan, Shankar M. Samsung R&D Inst Bangalore Karnataka India

ISBN: (数字)9783319592596

ISBN: (纸本)9783319592596;9783319592589

In this work, we propose a training algorithm for an audiovisual automatic speech recognition (AV-ASR) system using deep recurrent neural network (RNN). First, we train a deep RNN acoustic model with a Connectionist Temporal Classification (CTC) objective function. The frame labels obtained from the acoustic model are then used to perform a non-linear dimensionality reduction of the visual features using a deep bottleneck network. Audio and visual features are fused and used to train a fusion RNN. The use of bottleneck features for visual modality helps the model to converge properly during training. Our system is evaluated on GRID corpus. Our results show that presence of visual modality gives significant improvement in character error rate (CER) at various levels of noise even when the model is trained without noisy data. We also provide a comparison of two fusion methods: feature fusion and decision fusion.

关键词： Audio-visual speech recognition Connectionist Temporal Classification Recurrent neural network

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4th iapr TC 9 workshop on Multimodal pattern recognition of Social Signals in Human-Computer-Interaction, MPRSS 2016

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4th iapr TC 9 workshop on Multimodal pattern recognition of Social Signals in Human-Computer-Interaction, MPRSS 2016

ISBN: (纸本)9783319592589

The proceedings contain 13 papers. The special focus in this conference is on Multimodal pattern recognition of Social Signals in Human-Computer-Interaction. The topics include: Bimodal recognition of Cognitive Load Based on Speech and Physiological Changes;Human Mobility-pattern Discovery and Next-Place Prediction from GPS Data;Fusion Architectures for Multimodal Cognitive Load recognition;Performance Analysis of Gesture recognition Classifiers for Building a Human Robot Interface;On Automatic Question Answering Using Efficient Primal-Dual Models;Hierarchical Bayesian Multiple Kernel Learning Based Feature Fusion for Action recognition;Audio Visual Speech recognition Using Deep Recurrent neural networks;Audio-Visual recognition of Pain Intensity;The SenseEmotion Database: A Multimodal Database for the Development and Systematic Validation of an Automatic Painand Emotion-recognition System;Photometric Stereo for 3D Face Reconstruction Using Non Linear Illumination Models and Recursively Measured Action Units.

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Fusion Architectures for Multimodal Cognitive Load recognition 1

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4th iapr TC 9 workshop on pattern recognition of Social Signals in Human-Computer-Interaction (MPRSS)

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

ISBN: (数字)9783319592596

ISBN: (纸本)9783319592596;9783319592589

Knowledge about the users emotional state is important to achieve human like, natural Human Computer Interaction (HCI) in modern technical systems. Humans rely on implicit signals like body gestures and posture, vocal changes (e.g. pitch) and mimic expressions when communicating. We investigate the relation between them and human emotion, specifically when completing easy or difficult tasks. Additionally we include physiological data which also differ in changes of cognitive load. We focus 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, build uni modal classifiers which then are combined to a multimodal mental load estimation by the use of Markov Fusion networks (MFN) and Kalman Filter Fusion (KFF).

关键词： Human computer interaction

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Business perception based on sentiment analysis through deep neuronal networks for natural language processing 17th

Business perception based on sentiment analysis through deep...

引用

17th International Conference on Next Generation Teletraffic and Wired/Wireless Advanced networks and Systems, NEW2AN 2017, 10th Conference on Internet of Things and Smart Spaces, ruSMART 2017 and 3rd International workshop on Nano-scale Computing and Communications, NsCC 2017

作者： Vargas, Mónica Pineda Parra, Octavio José Salcedo Rico, Miguel José Espitia Departamento de Ingeniería de Sistemas e Industrial Universidad Nacional de Colombia Bogotá D.C. Colombia Facultad de Ingeniería Universidad Distrital "Francisco José de Caldas" Bogotá D.C. Colombia

ISBN: (纸本)9783319673790

In recent years, the machine-learning field, deep neural networks has been an important topic of research, used in several disciplines such as pattern recognition, information retrieval, classification and natural language processing. Is in the last that this paper it’s going to be our principal topic, in this branch exist an specific task that in literature is called Sentiment Analysis were the principal function is to detect if an opinion is positive or negative. In the paper we show how use this subset of the machine learning knowledge and use it for give us an insight in the question: what is the perception in a business or a product by means of the opinion of the consumers in social networks? © 2017, Springer International Publishing AG.

关键词： Sentiment analysis

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Spatio-temporal pain recognition in CNN-based super-resolved facial images 1

引用

3rd workshop on Video Analytics for Audience Measurement, VAAM 2016 and 2nd International workshop on Face and Facial Expression recognition, FFER 2016

作者： Bellantonio, Marco Haque, Mohammad A. Rodriguez, Pau Nasrollahi, Kamal Telve, Taisi Escarela, Sergio Gonzalez, Jordi Moeslund, Thomas B. Rasti, Pejman Anbarjafari, Gholamreza University of Barcelona Barcelona Spain Laboratory Aalborg University Aalborg Denmark iCV Research Group Institute of Technology University of Tartu Tartu Estonia

ISBN: (数字)9783319566870

ISBN: (纸本)9783319566863

Automatic pain detection is a long expected solution to a prevalent medical problem of pain management. This is more relevant when the subject of pain is young children or patients with limited ability to communicate about their pain experience. Computer vision-based analysis of facial pain expression provides a way of efficient pain detection. When deep machine learning methods came into the scene, automatic pain detection exhibited even better performance. In this paper, we figured out three important factors to exploit in automatic pain detection: spatial information available regarding to pain in each of the facial video frames, temporal axis information regarding to pain expression pattern in a subject video sequence, and variation of face resolution. We employed a combination of convolutional neural network and recurrent neural network to setup a deep hybrid pain detection framework that is able to exploit both spatial and temporal pain information from facial video. In order to analyze the effect of different facial resolutions, we introduce a super-resolution algorithm to generate facial video frames with different resolution setups. We investigated the performance on the publicly available UNBC-McMaster Shoulder Pain database. As a contribution, the paper provides novel and important information regarding to the performance of a hybrid deep learning framework for pain detection in facial images of different resolution. © Springer International Publishing AG 2017.

关键词： Recurrent neural 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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