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共 274 条记录，以下是131-140 订阅

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On graph-associated matrices and their eigenvalues for optical character recognition

On graph-associated matrices and their eigenvalues for optic...

引用

5th INNS iapr TC 3 GIRPR workshop on artificial neural networks for pattern recognition, ANNPR 2012

作者： Schmidt, Miriam Palm, Günther Schwenker, Friedhelm Institute of Neural Information Processing University of Ulm 89069 Ulm Germany

ISBN: (纸本)9783642332111

In this paper, the classification power of the eigenvalues of six graph-associated matrices is investigated and evaluated on a benchmark dataset for optical character recognition. The extracted eigenvalues were utilized as feature vectors for multi-class classification using support vector machines. Each graph-associated matrix contains a certain type of geometric/spacial information, which may be important for the classification process. Classification results are presented for all six feature types, as well as for classifier combinations at decision level. For the decision level combination probabilistic output support vector machines have been applied. The eigenvalues of the weighted adjacency matrix provided the best classification rate of 89.9%. Here, almost half of the misclassified letters are confusion pairs, such as I-L and N-Z. This classification performance can be increased by decision fusion, using the sum rule, to 92.4%. © 2012 Springer-Verlag.

关键词： Support vector machines

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Learning neural Models for End-to-End Clustering 8th

Learning Neural Models for End-to-End Clustering

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

作者： Meier, Benjamin Bruno Elezi, Ismail Amirian, Mohammadreza Duerr, Oliver Stadelmann, Thilo ZHAW Datalab Winterthur Switzerland Sch Engn Winterthur Switzerland ARGUS DATA INSIGHTS Schweiz AG Zurich Switzerland Ca Foscari Univ Venice Venice Italy Ulm Univ Inst Neural Informat Proc Ulm Germany HTWG Konstanz Inst Opt Syst Constance Germany

ISBN: (纸本)9783319999784;9783319999777

We propose a novel end-to-end neural network architecture that, once trained, directly outputs a probabilistic clustering of a batch of input examples in one pass. It estimates a distribution over the number of clusters k, and for each 1 <= k <= k(max), a distribution over the individual cluster assignment for each data point. The network is trained in advance in a supervised fashion on separate data to learn grouping by any perceptual similarity criterion based on pairwise labels (same/ different group). It can then be applied to different data containing different groups. We demonstrate promising performance on high-dimensional data like images (COIL-100) and speech (TIMIT). We call this "learning to cluster" and show its conceptual difference to deep metric learning, semi-supervise clustering and other related approaches while having the advantage of performing learnable clustering fully end-to-end.

关键词： Perceptual grouping Learning to cluster Speech & image clustering

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Sequence-to-Sequence CNN-BiLSTM Based Glottal Closure Instant Detection from Raw Speech 10th

Sequence-to-Sequence CNN-BiLSTM Based Glottal Closure Instan...

引用

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

作者： Matousek, Jindrich Tihelka, Daniel Univ West Bohemia Fac Appl Sci Dept Cybernet Plzen Czech Republic Univ West Bohemia New Technol Informat Soc NTIS Fac Sci Appl Plzen Czech Republic

ISBN: (纸本)9783031206498;9783031206504

In this paper, we propose to frame glottal closure instant (GCI) detection from raw speech as a sequence-to-sequence prediction problem and to explore the potential of recurrent neural networks (RNNs) to handle this problem. We compare the RNN architecture to widely used convolutional neural networks (CNNs) and to some other machine learning-based and traditional non-learning algorithms on several publicly available databases. We show that the RNN architecture improves GCI detection. The best results were achieved for a joint CNN-BiLSTM model in which RNN is composed of bidirectional long short-term memory (BiLSTM) units and CNN layers are used to extract relevant features.

关键词： Glottal closure instant detection Deep learning Recurrent neural network Convolutional neural network

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Joint iapr International workshops on Structural, Syntactic pattern recognition and Statistical Techniques in pattern recognition, SSPR 2000 and SPR 2000

Joint IAPR International Workshops on Structural, Syntactic ...

引用

International Association of pattern recognition, iapr 2000 held jointly with 8th International workshop on Structural and Syntactic pattern recognition, SSPR 2000 and 3rd International workshop on Statistical Techniques in pattern recognition, SPR 2000

ISBN: (纸本)3540679464

The proceedings contain 92 papers. The special focus in this conference is on Pierre Devijver Lecture and Hybrid and Combined Methods. The topics include: Some notes on twenty one 21 nearest prototype classifiers;adaptive graphical pattern recognition beyond connectionist-based approaches;current trends in grammatical inference;classifier's complexity control while training multilayer perceptrons;a framework for classifier fusion;image pattern recognition based on examples;a hybrid system for the recognition of hand-written characters;improving statistical measures of feature subsets by conventional and evolutionary approaches;selection of classifiers based on multiple classifier behaviour;the adaptive subspace map for image description and image database retrieval;adaptative automatic target recognition with SVM boosting for outlier detection;a multiresolution causal colour texture model;writer identification;syntactic pattern recognition by error correcting analysis on tree automata;offline recognition of syntax-constrained cursive handwritten text;structural classification for retrospective conversion of documents;segmentation of date field on bank cheques;grammars and discourse theory to describe and recognize mechanical assemblies;computation of the N best parse trees for weighted and stochastic context-free grammars;partitional vs hierarchical clustering using a minimum grammar complexity approach;encoding nondeterministic finite-state tree automata in sigmoid recursive neural networks;a structural matching algorithm using generalized deterministic annealing;alignment and correspondence using singular value decomposition;fast candidate elimination using machine learning techniques and efficient alignment and correspondence using edit distance.

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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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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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F-Measure Curves for Visualizing Classifier Performance with Imbalanced Data 8th

F-Measure Curves for Visualizing Classifier Performance with...

引用

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

作者： Soleymani, Roghayeh Granger, Eric Fumera, Giorgio Univ Quebec Ecole Technol Super Lab Imagerie Vis & Intelligence Artificielle Montreal PQ Canada Univ Cagliari Dept Elect & Elect Engn Pattern Recognit & Applicat Lab Cagliari Italy

ISBN: (纸本)9783319999784;9783319999777

Training classifiers using imbalanced data is a challenging problem in many real-world recognition applications due in part to the bias in performance that occur for: (1) classifiers that are often optimized and compared using unsuitable performance measurements for imbalance problems;(2) classifiers that are trained and tested on a fixed imbalance level of data, which may differ from operational scenarios;(3) cases where the preference of correct classification of classes is application dependent. Specialized performance evaluation metrics and tools are needed for problems that involve class imbalance, including scalar metrics that assume a given operating condition (skew level and relative preference of classes), and global evaluation curves or metrics that consider a range of operating conditions. We propose a global evaluation space for the scalar F-measure metric that is analogous to the cost curves for expected cost. In this space, a classifier is represented as a curve that shows its performance over all of its decision thresholds and a range of imbalance levels for the desired preference of true positive rate to precision. Experiments with synthetic data show the benefits of evaluating and comparing classifiers under different operating conditions in the proposed F-measure space over ROC, precision-recall, and cost spaces.

关键词： Class imbalance Performance visualization tools F-measure

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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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Comparative Study of Deep Learning Models in Melanoma Detection 11th

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

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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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pattern Classification Using a Penalized Likelihood Method

Pattern Classification Using a Penalized Likelihood Method

引用

4th workshop on artificial neural networks in pattern recognition

作者： Al-Ani, Ahmed Atiya, Amir F. Univ Technol Sydney Fac Engn & Informat Technol Sydney NSW 2007 Australia Cairo Univ Dept Comp Engn Giza Egypt

ISBN: (纸本)9783642121586

Penalized likelihood is a well-known theoretically justified approach that has recently attracted attention by the machine learning society. The objective function of the Penalized likelihood consists of the log likelihood of the data minus some term penalizing non-smooth solutions. Subsequently, maximizing this objective function would lead to some sort of trade-off between the faithfulness and the smoothness of the fit. There has been a lot of research to utilize penalized likelihood in regression, however, it is still to be thoroughly investigated in the pattern classification domain. We propose to use a penalty term based on the K-nearest neighbors and an iterative approach to estimate the posterior probabilities. In addition, instead of fixing the value of K for all pattern, we developed a variable K approach, where the number of neighbors can vary from one sample to another. The chosen value of K for a given testing sample is influenced by the K values of its surrounding training samples as well as the most successful K value of all training samples. Comparison with a number of well-known classification methods proved the potential of the proposed method.

关键词： Iterative methods

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