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共 274 条记录，以下是131-140 订阅

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Anomaly pattern recognition with Privileged Information for Sensor Fault Detection 8th

Anomaly Pattern Recognition with Privileged Information for ...

引用

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

作者： Smolyakov, Dmitry Sviridenko, Nadezda Burikov, Evgeny Burnaev, Evgeny Skolkovo Inst Sci & Technol Moscow Moscow Region Russia PO AO Minimaks 94 Moscow Russia

ISBN: (纸本)9783319999784;9783319999777

Detection of malfunction sensors is an important problem in the field of Internet of Things. One of the classical approaches to recognize anomalous patterns in sensor data is to use anomaly detection techniques based on One Class Classification like Support Vector Data Description or One Class Support Vector Machine. These techniques allow to build a "geometrical" model of a sensor regular operating state using historical data and detect broken sensors based on a distance to the regular data patterns. Usually important signals/warnings, which can help to identify broken sensors, arrive only after their failures. In this paper, we propose the approach to utilize such data by using the privileged information paradigm: we incorporate signals/warnings, available only when training the anomaly detection model, to refine the location of the boundary, separating the anomalous region. We demonstrate the approach by solving the problem of broken sensor detection in a Road Weather Information System.

关键词： Anomaly detection Road weather information system Learning using privileged information Internet of Things

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A new multi-class fuzzy support vector machine algorithm 6

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6th iapr TC3 International workshop on artificial neural networks for pattern recognition, ANNPR 2014

作者： Schwenker, Friedhelm Frey, Markus Glodek, Michael Kächele, Markus Meudt, Sascha Schels, Martin Schmidt, Miriam Ulm University Institute of Neural Information Processing Ulm89069 Germany

ISBN: (纸本)9783319116556

In this paper a novel approach to fuzzy support vector machines (SVM) in multi-class classification problems is presented. The proposed algorithm has the property to benefit from fuzzy labeled data in the training phase and can determine fuzzy memberships for input data. The algorithm can be considered as an extension of the traditional multi-class SVM for crisp labeled data, and it also extents the fuzzy SVM approach for fuzzy labeled training data in the two-class classification setting. Its behavior is demonstrated on three benchmark data sets, the achieved results motivate the inclusion of fuzzy labeled data into the training set for various tasks in pattern recognition and machine learning, such as the design of aggregation rules in multiple classifier systems, or in partially supervised learning. © Springer International Publishing Switzerland 2014.

关键词： pattern recognition

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Majority-class aware support vector domain oversampling for imbalanced classification problems 6

引用

6th iapr TC3 International workshop on artificial neural networks for pattern recognition, ANNPR 2014

作者： Kächele, Markus Thiam, Patrick Palm, Günther Schwenker, Friedhelm Institute of Neural Information Processing Ulm University James-Franck-Ring Ulm89081 Germany

ISBN: (纸本)9783319116556

In this work, a method is presented to overcome the difficulties posed by imbalanced classification problems. The proposed algorithm fits a data description to the minority class but in contrast to many other algorithms, awareness of samples of the majority class is used to improve the estimation process. The majority samples are incorporated in the optimization procedure and the resulting domain descriptions are generally superior to those without knowledge about the majority class. Extensive experimental results support the validity of this approach. © Springer International Publishing Switzerland 2014.

关键词： Data description

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Comparative Study of Deep Learning Models in Melanoma Detection 11th

Comparative Study of Deep Learning Models in Melanoma Detect...

引用

11th iapr TC3 workshop on artificial neural networks in pattern recognition (ANNPR)

作者： Haghshenas, Farnaz Krzyzak, Adam Osowski, Stanislaw Concordia Univ Dept Comp Sci & Software Engn Montreal PQ H3G 1M8 Canada Warsaw Univ Technol Fac Elect Engn Pl Politech 1 PL-00661 Warsaw Poland

ISBN: (纸本)9783031716010;9783031716027

The increasing number of skin cancers underscores the critical importance of early detection and accurate classification to improve treatment outcomes. Melanoma, a malignant skin cancer, has the highest mortality rate among all skin cancer types. Early detection of melanoma significantly enhances the chances of effective treatment and survival rates. This research evaluates advanced deep learning techniques in medical imaging, specifically Vision Transformers (ViT) and Convolutional neural networks (CNNs), for detecting melanoma. In this study, we used an annotated dataset of melanoma dermoscopic images. In addition, we employed the k-fold cross-validation technique to evaluate the reliability of our models. Our experimental results indicate that pre-trained Vision Transformers achieved an exceptional diagnostic accuracy of 97.97%, outperforming other models, specifically the pre-trained CNNs models.

关键词： Medical image analysis Deep learning Vision transformer Melanoma detection Skin cancer detection

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Active Learning for Speech Event Detection in HCI 7th

Active Learning for Speech Event Detection in HCI

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7th iapr TC3 workshop on artificial neural networks in pattern recognition (ANNPR)

作者： Thiam, Patrick Meudt, Sascha Schwenker, Friedhelm Palm, Guenther Univ Ulm Inst Neural Informat Proc D-89081 Ulm Germany

ISBN: (纸本)9783319461823;9783319461816

In this work, a pool-based active learning approach combining outlier detection methods with uncertainty sampling is proposed for speech event detection. Events in this case are regarded as atypical utterances (e.g. laughter, heavy breathing) occurring sporadically during a Human Computer Interaction (HCI) scenario. The proposed approach consists in using rank aggregation to select informative speech segments which have previously been ranked using different outlier detection techniques combined with an uncertainty sampling technique. The uncertainty sampling method is based on the distance to the boundary of a Support Vector Machine with Radial Basis Function kernel trained on the available annotated samples. Extensive experimental results prove the effectiveness of the proposed approach.

关键词： Active learning Supervised learning Support Vector Machines Support Vector Data Description Gaussian Mixture Model Rank aggregation

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Graph Augmentation for neural networks Using Matching-Graphs 10th

Graph Augmentation for Neural Networks Using Matching-Graphs

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10th iapr TC3 workshop on artificial neural networks for pattern recognition (ANNPR)

作者： Fuchs, Mathias Riesen, Kaspar Univ Bern Inst Comp Sci CH-3012 Bern Switzerland Univ Appl Sci Northwestern Inst Informat Syst CH-4600 Olten Switzerland

ISBN: (纸本)9783031206498;9783031206504

Both data access and data collection have become increasingly easy over the past decade, leading to rapid developments in many areas of intelligent information processing. In some cases, however, the amount of data is still not sufficiently large (e.g. in some machine learning applications). Data augmentation is a widely used mechanism to increase the available data in such cases. Current augmentation methods are mostly developed for statistical data and only a small part of these methods is directly applicable to graphs. In a recent research project, a novel encoding of pairwise graph matchings is introduced. The basic idea of this encoding, termed matching-graph, is to formalize the stable cores of pairs of patterns by means of graphs. In the present paper, we propose to use these matching-graphs to augment training sets of graphs in order to stabilize the training process of state-of-the-art graph neural networks. In an experimental evaluation on five graph data sets, we show that this novel augmentation technique is able to significantly improve the classification accuracy of three different neural network models.

关键词： Graph matching Matching-graphs Graph edit distance Graph augmentation GNN

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artificial neural networks in pattern recognition 1

引用

丛书名： Lecture Notes in Computer Science

1000年

作者： Nadia Mana Friedhelm Schwenker Edmondo Trentin

ISBN: (数字)9783642332128

ISBN: (纸本)9783642332111

This book constitutes the refereed proceedings of the 5th INNS iapr TC3 GIRPR International workshop on artificial neural networks in pattern recognition, ANNPR 2012, held in Trento, Italy, in September 2012. The 21 revised full papers presented were carefully reviewed and selected for inclusion in this volume. They cover a large range of topics in the field of neural network- and machine learning-based pattern recognition presenting and discussing the latest research, results, and ideas in these areas.

关键词： artificial Intelligence pattern recognition Data Mining and Knowledge Discovery Image Processing and Computer Vision User Interfaces and Human Computer Interaction Computer Imaging, Vision, pattern recognition and Graphics

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Disparity using feature points in multi scale 9th

引用

Joint iapr 9th International workshop on Structural and Syntactic pattern recognition, SSPR 2002 and 4th International workshop on Statistical Techniques in pattern recognition, SPR 2002

作者： Ulusoy, Ilkay Hancock, Edwin R. Halici, Ugur Computer Vision and Artificial Neural Networks Lab Middle East Technical University Ankara Turkey Department of Computer Science Uni versity of York YorkY01 5DD United Kingdom

ISBN: (纸本)3540440119

In this paper we describe a statistical framework for binocular disparity estimation. We use a bank of Gabor filters to compute multiscale phase signatures at detected feature points. Using a von Mises distribution, we calculate correspondence probabilities for the feature points in different images using the phase differences at different scales. The disparity map is computed using the set of maximum likelihood correspondences. © Springer-Verlag Berlin Heidelberg 2002.

关键词： Gabor filters

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artificial neural networks in pattern recognition 1

引用

丛书名： Lecture Notes in Computer Science

1000年

作者： Neamat El Gayar Edmondo Trentin Mirco Ravanelli Hazem Abbas

ISBN: (数字)9783031206504

ISBN: (纸本)9783031206498

This book constitutes the refereed proceedings of the 10th iapr TC3 International workshop on artificial neural networks in pattern recognition, ANNPR 2022, held in Dubai, UAE, in November 2022. The 16 revised full papers presented were carefully reviewed and selected from 24 submissions. The conference presents papers on subject such as pattern recognition and machine learning based on artificial neural networks.

关键词： artificial Intelligence Computers and Education Data Mining and Knowledge Discovery Information Systems Applications (incl. Internet) Computer Appl. in Social and Behavioral Sciences Image Processing and Computer Vision

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Manifold Learning Regression with Non-stationary Kernels 8th

Manifold Learning Regression with Non-stationary Kernels

引用

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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