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Polyphonic monotimbral music transcription using dynamic networks

pattern recognition LETTERS 2005年第12期26卷 1809-1818页

作者： Pertusa, A Inesta, JM Univ Alicante Dept Lenguajes & Sistemas Informat E-03080 Alicante Spain

The automatic extraction of the notes that were played in a digital musical signal (automatic music transcription) is an open problem. A number of techniques have been applied to solve it without concluding results. The monotimbral polyphonic version of the problem is posed here: a single instrument has been played and more than one note can sound at the same time. This work tries to approach it through the identification of the pattern of a given instrument in the frequency domain. This is achieved using time-delay neural networks that are fed with the band-grouped spectrogram of a polyphonic monotimbral music recording. The use of a learning scheme based on examples like neural networks permits our system to avoid the use of an auditory model to approach this problem. A number of issues have to be faced to have a robust and powerful system, but promising results using synthesized instruments are presented. (c) 2005 Elsevier B.V. All rights reserved.

关键词： COGNITIVE neuroscience neural networks (Computer science) artificial intelligence ROBUst control

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Recursive neural networks learn to localize faces

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pattern recognition LETTERS 2005年第12期26卷 1885-1895页

作者： Bianchini, M Maggini, M Sarti, L Scarselli, F Univ Siena Dipartimento Ingn Informaz I-53100 Siena Italy

Localizing faces in images is a difficult task, and represents the first step towards the solution of the face recognition problem. Moreover, devising an effective face detection method can provide some suggestions to solve similar object and pattern detection problems. This paper presents a novel approach to the solution of the face localization problem using Recursive neural networks (RNNs). The proposed method assumes a graph-based representation of images that combines structural and symbolic visual features. Such graphs are then processed by RNNs, in order to establish the possible presence and the position of faces inside the image. A novel RNN model that can deal with graphs with labeled edges has been also exploited. Some experiments on snapshots from video sequences are reported, showing very promising results. (c) 2005 Elsevier B.V. All rights reserved.

关键词： recursive neural networks face recognition localization of faces

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Sign-based learning schemes for pattern classification

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pattern recognition LETTERS 2005年第12期26卷 1926-1936页

作者： Anastasiadis, AD Magoulas, GD Vrahatis, MN Univ London Birkbeck Coll Sch Comp Sci & Informat Syst Knowledge Lab London WC1N 3QS England Univ London Birkbeck Coll Sch Comp Sci & Informat Syst London WC1E 7HX England Univ Patras UPAIRC Dept Math GR-26110 Patras Greece

This paper introduces a new class of sign-based training algorithms for neural networks that combine the sign-based updates of the Rprop algorithm with the composite nonlinear Jacobi method. The theoretical foundations of the class are described and a heuristic Rprop-based Jacobi algorithm is empirically investigated through simulation experiments in benchmark pattern classification problems. Numerical evidence shows that this new modification of the Rprop algorithm exhibits improved learning speed in all cases tested, and compares favorably against the Rprop and a recently proposed modification, the improved Rprop. (c) 2005 Elsevier B.V. All rights reserved.

关键词： supervised learning nonlinear iterative methods nonlinear Jacobi subminimization feed-forward neural networks Rprop pattern classification

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Unsupervised spatial pattern classification of electrical-wafer-sorting maps in semiconductor manufacturing

Unsupervised spatial pattern classification of electrical-wa...

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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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Exploratory basis pursuit classification

Exploratory basis pursuit classification

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1st iapr tc3 workshop on artificial neural networks in pattern recognition

作者： Brown, M Costen, NP Univ Manchester Control Syst Ctr Sch Elect & Elect Engn Manchester M60 1QD Lancs England Manchester Metropolitan Univ Dept Comp & Math Manchester Lancs England

Feature selection is a fundamental process in many classifier design problems. However, it is NP-complete and approximate approaches often require requires extensive exploration and evaluation. This paper describes a novel approach that represents feature selection as a continuous regularization problem which has a single, global minimum, where the model's complexity is measured using a 1-norm on the parameter vector. A new exploratory design process is also described that allows the designer to efficiently construct the complete locus of sparse, kernel-based classifiers. It allows the designer to investigate the optimal parameters' trajectories as the regularization parameter is altered and look for effects, such as Simpson's paradox, that occur in many multivariate data analysis problems. The approach is demonstrated on the well-known Australian Credit data set. (c) 2005 Published by Elsevier B.V.

关键词： feature selection sparse classification regularization

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Wide coverage natural language processing using kernel methods and neural networks for structured data

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pattern recognition LETTERS 2005年第12期26卷 1896-1906页

作者： Menchetti, S Costa, F Frasconi, P Pontil, M Univ Florence Dept Comp Sci & Syst I-50139 Florence Italy UCL Dept Comp Sci London WC1E 6BT England

Convolution kernels and recursive neural networks are both suitable approaches for supervised learning when the input is a discrete structure like a labeled tree or graph. We compare these techniques in two natural language problems. In both problems, the learning task consists in choosing the best alternative tree in a set of candidates. We report about an empirical evaluation between the two methods on a large corpus of parsed sentences and speculate on the role played by the representation and the loss function. (c) 2005 Elsevier B.V. All rights reserved.

关键词： convolution kernels recursive neural networks learning preferences natural language

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Time series forecasting: Obtaining long term trends with self-organizing maps

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pattern recognition LETTERS 2005年第12期26卷 1795-1808页

作者： Simon, G Lendasse, A Cottrell, M Fort, JC Verleysen, M Univ Catholique Louvain DICE Machine Learning grp B-1348 Louvain Belgium Helsinki Univ Technol Lab Comp & Informat Sci Neural Networks Res Ctr FIN-02015 Espoo Finland Univ Paris 01 CNRS UMR 8595 Samos Matisse F-75634 Paris France Univ Toulouse 3 CNRS C55830 Lab Stat & Probabil F-31062 Toulouse France

Kohonen self-organisation maps are a well know classification tool, commonly used in a wide variety of problems, but with limited applications in time series forecasting context. In this paper, we propose a forecasting method specifically designed for multi-dimensional long-term trends prediction, with a double application of the Kohonen algorithm. Practical applications of the method are also presented. (c) 2005 Elsevier B.V. All rights reserved.

关键词： self-organizing maps time series forecasting long-term trends

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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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Hybrid generative/discriminative classifier for unconstrained character recognition

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pattern recognition LETTERS 2005年第12期26卷 1840-1848页

作者： Prevost, L Oudot, L Moises, A Michel-Sendis, C Milgram, M Univ Paris 06 Lab Instruments & Syst Ile France Grp PARC F-75252 Paris France

Handwriting recognition for hand-held devices like PDAs requires very accurate and adaptive classifiers. It is such a complex classification problem that it is quite usual now to make co-operate several classification methods. In this paper, we present an original two stages recognizer. The first stage is a model-based classifier which store an exhaustive set of character models. The second stage is a pairwise classifier which separate the most ambiguous pairs of classes. This hybrid architecture is based on the idea that the correct class almost systematically belongs to the two more relevant classes found by the first classifier. Experiments on a 80,000 examples database show a 30% improvement on a 62 classes recognition problem. Moreover, we show experimentally that such an architecture suits perfectly for incremental classification. (c) 2005 Elsevier B.V. All rights reserved.

关键词： handwriting recognition multiple classifier system pairwise neural networks confusion matrix adaptive classifier

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Exploratory basis pursuit classification

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pattern recognition LETTERS 2005年第12期26卷 1907-1915页

作者： Brown, M Costen, NP Univ Manchester Control Syst Ctr Sch Elect & Elect Engn Manchester M60 1QD Lancs England Manchester Metropolitan Univ Dept Comp & Math Manchester Lancs England

关键词： feature selection sparse classification regularization

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