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4th workshop on artificial neural networks in pattern recognition

作者： Trentin, Edmondo Zhang, ShuJia Hagenbuchner, Markus DII Univ Siena V Roma 56 Siena Italy Univ Wollongong Wollongong NSW 2522 Australia

ISBN: (纸本)9783642121586

Several real-world problems (e.g., in bioinformatics/proteomics, or in recognition of video sequences) can be described as classification tasks over sequences of structured data, i.e. sequences of graphs, in a natural way. This paper presents a novel machine that can learn and carry out decision-making over sequences of graphical data. The machine involves a hidden Markov model whose state-emission probabilities are defined over graphs. This is realized by combining recursive encoding networks and constrained radial basis function networks. A global optimization algorithm which regards to the machine as a unity (instead of a bare superposition of separate modules) is introduced, via gradient-ascent over the maximum-likelihood criterion within a Baum-Welch-like forward-backward procedure. To the best of our knowledge, this is the first machine learning approach capable of processing sequences of graphs without the need of a pre-processing step. Preliminary results are reported.

关键词： Hidden Markov model relational learning recursive networks

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Maximum Echo-State-Likelihood networks for Emotion recognition

Maximum Echo-State-Likelihood Networks for Emotion Recogniti...

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4th workshop on artificial neural networks in pattern recognition

作者： Trentin, Edmondo Scherer, Stefan Schwenker, Friedhelm Univ Siena Dipartimento Ingn Informazione Via Laterina 8 I-53100 Siena Italy Univ Ulm Inst Neural Informa Proc D-89069 Ulm Germany

ISBN: (纸本)9783642121586

Emotion recognition is a relevant task in human-computer interaction. Several pattern recognition and machine learning techniques have been applied so far in order to assign input audio and/or video sequences to specific emotional classes. This paper introduces a novel approach to the problem, suitable also to more generic sequence recognition tasks. The approach relies on the combination of the recurrent reservoir of an echo state network with a connectionist density estimation module. The reservoir realizes an encoding of the input sequences into a fixed-dimensionality pattern of neuron activations. The density estimator, consisting of a constrained radial basis functions network, evaluates the likelihood of the echo state given the input. Unsupervised training is accomplished within a maximum-likelihood framework. The architecture can then be used for estimating class-conditional probabilities in order to carry out emotion classification within a Bayesian setup. Preliminary experiments in emotion recognition from speech signals from the WaSeP (c) dataset show that the proposed approach is effective, and it may outperform state-of-the-art classifiers.

关键词： Emotion recognition echo state network radial basis functions maximum likelihood density estimation

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Bayesian Learning of Generalized Gaussian Mixture Models on Biomedical Images

Bayesian Learning of Generalized Gaussian Mixture Models on ...

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4th workshop on artificial neural networks in pattern recognition

作者： Elguebaly, Tarek Bouguila, Nizar Concordia Univ Fac Engn & Comp Sci CIISE Montreal PQ H3G 2W1 Canada

ISBN: (纸本)9783642121586

In the context of biomedical image processing and bioinformatics, an important problem is the development of accurate models for image segmentation and DNA spot detection. In this paper we propose a highly efficient unsupervised Bayesian algorithm for biomedical image segmentation and spot detection of cDNA microarray images, based on generalized Gaussian mixture models. Our work is motivated by the fact that biomedical and cDNA microarray images both contain non-Gaussian characteristics, impossible to model using rigid distributions like the Gaussian. Generalized Gaussian mixture models are robust in the presence of noise and outliers and are more flexible to adapt the shape of data.

关键词： Bioinformatics

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Exploiting neural networks to Enhance Trend Forecasting for Hotels Reservations

Exploiting Neural Networks to Enhance Trend Forecasting for ...

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4th workshop on artificial neural networks in pattern recognition

作者： Zakhary, Athanasius El Gayar, Neamat Ahmed, Sanaa El-Ola H. Cairo Univ Fac Comp & Informat Giza 12613 Egypt

ISBN: (纸本)9783642121586

Hotel revenue management is perceived as a managerial tool for room revenue maximization. A typical revenue management system contains two main components: Forecasting and Optimization. A forecasting component that gives accurate forecasts is a cornerstone in any revenue management system. It simply draws a good picture for the future demand. The output of the forecast component is then used for optimization and allocation in such a way that maximizes revenue. This shows how it is important to have a reliable and precise forecasting system. neural networks have been successful in forecasting in many fields. In this paper, we propose the use of NN to enhance the accuracy of a Simulation based Forecasting system, that was developed in an earlier work. In particular a neural network is used for modeling the trend component in the simulation based forecasting model. In the original model, Holt's technique was used to forecast the trend. In our experiments using real hotel data we demonstrate that the proposed neural network approach outperforms the Holt's technique. The proposed enhancement also resulted in better arrivals and occupancy forecasting when incorporated in the simulation based forecasting system.

关键词： Hotels

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pattern Classification Using a Penalized Likelihood Method

Pattern Classification Using a Penalized Likelihood Method

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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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Content-Based Retrieval and Classification of Ultrasound Medical Images of Ovarian Cysts

Content-Based Retrieval and Classification of Ultrasound Med...

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4th workshop on artificial neural networks in pattern recognition

作者： Sohail, Abu Sayeed Md Bhattacharya, Prabir Mudur, Sudhir P. Krishnamurthy, Srinivasan Gilbert, Lucy Concordia Univ Dept Comp Sci & Software Engn Montreal PQ Canada Univ Cincinnati Dept Comp Sci Cincinnati OH 45221 USA Royal Victoria Hosp Dept Obstet & Gynaecol Montreal PQ H3A 1A1 Canada

ISBN: (纸本)9783642121586

This paper presents a combined method of content-based retrieval and classification of ultrasound medical images representing three types of ovarian cysts: Simple Cyst, Endometrioma, and Teratoma. Combination of histogram moments and Gray Level Co-Occurrence Matrix (GLCM) based statistical texture descriptors has been proposed as the features for retrieving and classifying ultrasound images. To retrieve images, relevance between the query image and the target images has been measured using a similarity model based on Gower's similarity coefficient. Image classification has been performed applying Fuzzy k-Nearest Neighbour (k-NN) classification technique. A database of 478 ultrasound ovarian images has been used to verify the retrieval and classification accuracy of the proposed system. In retrieving ultrasound images, the proposed method has demonstrated above 79% and 75% of average precision considering the first 20 and 40 retrieved images respectively. Further, 88.12% of average classification accuracy has been achieved in classifying ultrasound images using the proposed method.

关键词： Ultrasound Medical Image Retrieval Ovarian Cyst Classification Texture Feature Histogram Moments Fuzzy k-NN

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The application of multiple model fusion based on correctable weight in tool wear pattern recognizing

The application of multiple model fusion based on correctabl...

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3rd International workshop on Advanced Computational Intelligence, IWACI 2010

作者： Chen, Kan Fu, Pan Cao, Weiqing Li, Weilin Department of Mechanic School Southwest JIAOTONG University Sichuan Province China

ISBN: (纸本)9781424463343

This paper treats an important problem in how to increase the accuracy of the tool wear condition evaluation. The problem of tool wear condition monitoring is always hard to be solved perfectly, especially the online monitoring. In order to arrive at the destination, the authors make use of artificial neural networks (BP) and support vector machines (RBF-SVM) which are two ways to build predictive models independently and then fuse each model predictions by D-S (Dempster/Shafer) in decision-making level. And a new parameter which is called model correcting weight ρ is introduced in to adjust the final contribution rate of each recognizing model. The weight is got by a black box process. In fact two series independent experiment data for each one tool wear pattern are prepared: Cdata1 and Cdata2. Use the Cdata1 (the first repeat experiment data) to train a model and predict the tool wear pattern. Some recognizing accuracy is got in this process. then the recognizing accuracy is regarded as the weight ρwhich is used to adjust the tool wear condition monitoring models which are trained by Cdata2 (the second repeat experiment data that correspond to Cdata1 but have deferent use). And the final experiment results show that the monitoring system clarifies the four states of the tool.

关键词： Support vector machines

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Lecture Notes in Computer Science (including subseries Lecture Notes in artificial Intelligence and Lecture Notes in Bioinformatics): Preface

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in artificial Intelligence and Lecture Notes in Bioinformatics) 2010年 5998 LNAI卷

作者： El Gayar, Neamat Schwenker, Friedhelm University of Ulm Department of Neural Information Processing Oberer Eselsberg 89069 Ulm Germany Egypt

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Numerical-logical processing in neural networks for the decision support

Numerical-logical processing in Neural Networks for the deci...

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International workshop on Soft Computing Applications (SOFA)

作者： Viorel Ariton Doinita Ariton Danubius¿ University Romania Danubius University Romania

artificial neural networks (ANN) embed shallow knowledge through learning. Used in diagnosis and decision support, ANN are immediate computational models for effects and causes as from human experience but keep out from the deep knowledge of them. The paper presents a way of embedding logical processing over the numerical ones in ldquoneural logical sitesrdquo for the classical ANN paradigms, then proposes a way of structuring deep knowledge in the network for all types of abduction problems in a unified way, which is compared with similar attempt. The approach may be spread in any diagnosis and decision support applications involving deep and shallow knowledge.

关键词： neural networks artificial neural networks Neurons Humans Computational modeling Problem-solving Diagnostic expert systems Shape pattern recognition Decision making

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Real-time emotion recognition from speech using echo state networks

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3rd iapr workshop on artificial neural networks in pattern recognition

作者： Scherer, Stefan Oubbati, Mohamed Schwenker, Friedhelm Palm, Guenther Univ Ulm Inst Neural Informat Proc D-89069 Ulm Germany

ISBN: (纸本)9783540699385

The goal of this work is to investigate real-time emotion recognition in noisy environments. Our approach is to solve this problem using novel recurrent neural networks called echo state networks (ESN). ESNs utilizing the sequential characteristics of biologically motivated modulation spectrum features are easy to train and robust towards noisy real world conditions. The standard Berlin Database of Emotional Speech is used to evaluate the performance of the proposed approach. The experiments reveal promising results overcoming known difficulties and drawbacks of common approaches.

关键词： Recurrent neural networks

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