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1st iapr TC3 workshop on artificial neural networks in pattern recognition

作者： Di Palma, F De Nicolao, G Miraglia, G Pasquinetti, E Piccinini, F Univ Pavia Dipartimento Informat & Sistemist I-27100 Pavia Italy STMicroelect I-20041 Agrate Brianza Italy

in semiconductor manufacturing, the spatial pattern of failed devices in a wafer can give precious hints on which step of the process is responsible for the failures. In the literature, Kohonen's Self Organizing Feature Maps (SOM) and Adaptive Resonance Theory 1 (ART1) architectures have been compared, concluding that the latter are to be preferred. However, both the simulated and the real data sets used for validation and comparison were very limited. In this paper, the use of ART1 and SOM as wafer classifiers is re-assessed on much more extensive simulated and real data sets. We conclude that ART1 is not adequate, whereas SOM provide completely satisfactory results including visually effective representation of spatial failure probability of the pattern classes. (c) 2005 Elsevier B.V. All rights reserved.

关键词： semiconductor manufacturing wafer maps self organizing feature maps adaptive resonance theory

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Leveraging LSTM Embeddings for River Water Temperature Modeling 11th

Leveraging LSTM Embeddings for River Water Temperature Model...

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

作者： Fankhauser, Benjamin Bigler, Vidushi Riesen, Kaspar Univ Bern Inst Comp Sci Bern Switzerland Bern Univ Appl Sci Inst Optimisat & Data Anal Biel Switzerland

ISBN: (纸本)9783031716010;9783031716027

River water temperature modeling is a major task in climate research. State-of-the-art methods for water temperature modeling deploy a transductive design, which makes it difficult to generalize to unseen water stations during test time. In the present paper, we isolate one of the common building blocks - a central LSTM, trained for each water station - and propose an embedding scheme in order to increase both the prediction accuracy and the amount of shared parameters and thus the generalization. The proposed embeddings are learned during training time. In an empirical evaluation we show that our method is able to reduce the RMSE by about five percentage points compared to the state-of-the-art reference method while decreasing the tuneable parameters by several orders of magnitude. We also provide a sample analysis of the embedding space of the catchment area of one specific river. Looking at the results of this qualitative analysis, we come to the conclusion that deploying an embedding in water temperature models is not only convincing to decrease the RMSE of water temperature predictions, but also enables better explainable deep learning models. Moreover, the proposed embedding technique opens up various unexplored applications in water temperature research.

关键词： Water Temperature Embedding LSTM Recurrent neural Network

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An improved handwritten Chinese character recognition system using support vector machine

An improved handwritten Chinese character recognition system...

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1st iapr TC3 workshop on artificial neural networks in pattern recognition

作者： Dong, JX Krzyzak, A Suen, CY Ctr Pattern Regcognit & Machine Intelligence Montreal PQ H3G 1M8 Canada Concordia Univ Dept Comp Sci & Software Engn Montreal PQ H3G 1M8 Canada

This paper describes several techniques improving a Chinese character recognition system. Enhanced nonlinear normalization, feature extraction and tuning kernel parameters of support vector machine on a large data set with thousands of classes, contribute to improvement of the overall system performance. The enhanced nonlinear normalization method not only solves the aliasing problem in the original Yamada et al.'s nonlinear normalization method but also avoids the undue stroke distortion in the peripheral region of the normalized image. The support vector machine is for the first time tested on a large data set composed of several million samples and thousands of classes. The recognition system has achieved a high recognition rate of 99.0% on ETL9B, a handwritten Chinese character database. (c) 2005 Elsevier B.V. All rights reserved.

关键词： normalization support vector machine Chinese characters recognition feature extraction

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Minimizing Cross Intersections in Graph Drawing via Linear Splines 10th

Minimizing Cross Intersections in Graph Drawing via Linear S...

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

作者： Hussain, Rida Ghafoor Tiezzi, Matteo Ciravegna, Gabriele Gori, Marco Univ Florence Florence Italy Univ Siena Siena Italy Univ Cote Azur Nice France

ISBN: (纸本)9783031206498;9783031206504

The generation of aesthetically pleasing graph layouts is the main purpose of Graph Drawing techniques. Recent contributions delved into the usage of Gradient-descent (GD) based schemes to optimize differentiable loss functions, built to measure the graph layout adherence to given layout characteristics. However, some properties cannot be easily expressed via differentiable functions. In this direction, the recently proposed Graph neural Drawer (GND) framework proposes to exploit the representational capability of neural models in order to be able to express differentiable losses, specifically for edge intersection, that can be subsequently optimized via GD. In this paper, we propose to improve graph layout readability leveraging linear splines. We exploit the principles behind GND and use a neural model both to identify crossing edges and to optimize their relative position. We split crossing edges introducing linear splines, and threat the control points as novel "fake" vertices that can be optimized via the underlying layout optimization process. We provide qualitative and quantitative analysis over multiple graphs and optimizing different aesthetic losses, that show how the proposed method is a viable solution.

关键词： Graph Drawing Gradient Descent Graph neural Drawers Splines

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An improved handwritten Chinese character recognition system using support vector machine

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pattern recognition LETTERS 2005年第12期26卷 1849-1856页

作者： Dong, JX Krzyzak, A Suen, CY Ctr Pattern Regcognit & Machine Intelligence Montreal PQ H3G 1M8 Canada Concordia Univ Dept Comp Sci & Software Engn Montreal PQ H3G 1M8 Canada

关键词： normalization support vector machine Chinese characters recognition feature extraction

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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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Deep Multi-label Classification of Personality with Handwriting Analysis 11th

Deep Multi-label Classification of Personality with Handwrit...

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

作者： Shamsabad, Marzieh Adeli Suen, Ching Yee Concordia Univ Ctr Pattern Recognit & Machine Intelligence CENPA Montreal PQ H3G 1M8 Canada

ISBN: (纸本)9783031716010;9783031716027

Handwriting analysis has traditionally been used to infer personality traits from the stylistic features of writing. With advances in machine learning, the accuracy and applicability of these analyses have significantly improved. This paper presents a new multi-label classification approach to classify personality traits, such as Extraversion and Conscientiousness, into Low, Average, and High categories. This approach uses Binary Cross-Entropy with Logits Loss and Focal Loss to handle multi-label classification and class imbalance. Image segmentation techniques are also employed to enhance the handling of limited handwriting samples. The paper evaluates the complexity and performance of ResNet-50 and ResNet-101 architectures in recognizing complex handwriting patterns using three optimizers: SGD with momentum, Adam, and AdaBelief. The results demonstrate the efficacy of our proposed method, improving overall accuracy from 67.09% to 90.16% for ResNet-50 and from 69.43% to 90.07% for ResNet-101, with an overall AUC of 0.96. These improvements emphasize the model's capability for practical automated handwriting analysis.

关键词： Handwriting Multi-label classification Class Imbalance Binary Cross-Entropy with Logits Loss Focal Loss

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Pitfalls in Processing Infinite-Length Sequences with Popular Approaches for Sequential Data 11th

Pitfalls in Processing Infinite-Length Sequences with Popula...

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

作者： Casoni, Michele Guidi, Tommaso Tiezzi, Matteo Betti, Alessandro Gori, Marco Melacci, Stefano Univ Siena DIISM I-52100 Siena Italy IMT Scuola Alti Studi I-55100 Lucca Italy

ISBN: (纸本)9783031716010;9783031716027

One of the enduring challenges for the Machine Learning community is developing models that can process and learn from very long data sequences. Transformer-based models and Recurrent neural networks (RNNs) have excelled in processing long sequences, yet face challenges in transitioning to processing infinite-length sequences online, a crucial step in mimicking human learning over continuous data streams. While Transformer models handle large context windows efficiently, they suffer from quadratic computational costs, motivating research into alternative attention mechanisms. Conversely, RNNs, particularly Deep State-Space Models (SSMs), have shown promise in long sequence tasks, outperforming Transformers in certain benchmarks. However, current approaches are limited to finite-length sequences, which are pre-buffered and randomly shuffled to cope with stochastic gradient descent. This paper addresses the fundamental gap in transitioning from offline-processing of a dataset of sequences to online-processing of possibly infinite-length sequences, a scenario often neglected in existing research. Empirical evidence is presented, demonstrating the performance and limits of existing models. We highlight the challenges and opportunities in learning from a continuous data stream, paving the way for future research in this area.

关键词： Recurrent neural networks Transformers Long Sequences Online Learning Learning over Time

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RBF neural networks for hand-based biometric recognition

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3rd International Conference on Audio- and Video- Based Biometric Person Authentication, AVBPA 2001

作者： Sanchez-Reillo, Raul Sanchez-Avila, Carmen Carlos III University of Madrid Dpt. Electric Electronic and Automatic Engineering c/Butarque 15 E-28911 Leganes Madrid Spain Polytechnic University of Madrid E.T.S.I. Telecomunicacion Dpt. Applied Mathematics Ciudad Universitaria S/n E-28040 Madrid Spain

ISBN: (纸本)3540422161

A recognition system based on hand geometry biometrics is reported in this paper. The aim of this development is to improve the reliability of automatic identification systems. The capture of the data, as well as the pre-processing and feature extraction blocks is detailed. Once the features are obtained, they should enter the recognition block, which has been developed using neural networks. From the different neural networks existing nowadays, the Radial Basis Functions (RBF) ones have been chosen for their shorter training time, and the lack of randomness in the training algorithm. Results are analyzed as a function of the number of vectors of each user taken for the training of the net, obtaining up to 98,1% for only 5 samples of each user. © Springer-Verlag 2001.

关键词： Biometrics

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Representative prototype sets for data characterization and classification

Representative prototype sets for data characterization and ...

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

作者： Lausser, Ludwig Müssel, Christoph Kestler, Hans A. Research Group Bioinformatics and Systems Biology Institute of Neural Information Processing Ulm University Ulm 89069 Germany

ISBN: (纸本)9783642332111

Common classifier models are designed to achieve high accuracies, while often neglecting the question of interpretability. In particular, most classifiers do not allow for drawing conclusions on the structure and quality of the underlying training data. By keeping the classifier model simple, an intuitive interpretation of the model and the corresponding training data is possible. A lack of accuracy of such simple models can be compensated by accumulating the decisions of several classifiers. We propose an approach that is particularly suitable for high-dimensional data sets of low cardinality, such as data gained from high-throughput biomolecular experiments. Here, simple base classifiers are obtained by choosing one data point of each class as a prototype for nearest neighbour classification. By enumerating all such classifiers for a specific data set, one can obtain a systematic description of the data structure in terms of class coherence. We also investigate the performance of the classifiers in cross-validation experiments by applying stand-alone prototype classifiers as well as ensembles of selected prototype classifiers. © 2012 Springer-Verlag.

关键词： Clustering algorithms

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