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...
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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.
Emotion recognition is a relevant task in human-computer interaction. Several patternrecognition and machine learning techniques have been applied so far in order to assign input audio and/or video sequences to speci...
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ISBN:
(纸本)9783642121586
Emotion recognition is a relevant task in human-computer interaction. Several patternrecognition 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.
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 u...
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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.
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 gi...
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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. neuralnetworks 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.
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 likel...
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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.
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 histo...
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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.
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 ...
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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 artificialneuralnetworks (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.
artificialneuralnetworks (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 fr...
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artificialneuralnetworks (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.
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 neuralnetworks called echo state networks (ESN). ESNs utilizing...
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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 neuralnetworks 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.
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