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 ...
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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.
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 pha...
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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 algorith...
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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...
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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 neuralnetworks (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.
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. l...
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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.
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...
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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 neuralnetworks. 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.
This book constitutes the refereed proceedings of the 5th INNS iapr TC3 GIRPR International workshop on artificialneuralnetworks in patternrecognition, ANNPR 2012, held in Trento, Italy, in September 2012. The 21 r...
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ISBN:
(数字)9783642332128
ISBN:
(纸本)9783642332111
This book constitutes the refereed proceedings of the 5th INNS iapr TC3 GIRPR International workshop on artificialneuralnetworks in patternrecognition, 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 patternrecognition presenting and discussing the latest research, results, and ideas in these areas.
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, w...
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This book constitutes the refereed proceedings of the 10th iapr TC3 International workshop on artificialneuralnetworks in patternrecognition, ANNPR 2022, held in Dubai, UAE, in November 2022. The 16 revised full pa...
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ISBN:
(数字)9783031206504
ISBN:
(纸本)9783031206498
This book constitutes the refereed proceedings of the 10th iapr TC3 International workshop on artificialneuralnetworks in patternrecognition, 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 patternrecognition and machine learning based on artificialneuralnetworks.
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 g...
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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.
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