Mixture modeling is the problem of identifying and modeling components in a given set of data. Gaussians are widely used in mixture modeling. At the same time, other models such as Dirichlet distributions have not rec...
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Mixture modeling is the problem of identifying and modeling components in a given set of data. Gaussians are widely used in mixture modeling. At the same time, other models such as Dirichlet distributions have not received attention. In this paper, we present an unsupervised algorithm for learning a finite Dirichlet mixture model. The proposed approach for estimating the parameters of a Dirichlet mixture is based on the maximum likelihood (ML) expressed in a Riemannian space. Experimental results are presented for the following applications: summarization of texture image databases for efficient retrieval, and human skin color modeling and its application to skin detection in multimedia databases. (c) 2005 Elsevier B.V. All rights reserved.
In this paper, we present experiments comparing different training algorithms for Radial Basis Functions (RBF) neuralnetworks. In particular we compare the classical training which consist of an unsupervised training...
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ISBN:
(纸本)3540379517
In this paper, we present experiments comparing different training algorithms for Radial Basis Functions (RBF) neuralnetworks. In particular we compare the classical training which consist of an unsupervised training of centers followed by a supervised training of the weights at the output, with the full supervised training by gradient descent proposed recently in same papers. We conclude that a fully supervised training performs generally better. We also compare Batch training with Online training and we conclude that Online training suppose a reduction in the number of iterations.
The proceedings contain 13 papers. The topics discussed include: generative modeling of dyadic conversations: characterization of pragmatic skills during development age;social coordination assessment: distinguishing ...
ISBN:
(纸本)9783642370809
The proceedings contain 13 papers. The topics discussed include: generative modeling of dyadic conversations: characterization of pragmatic skills during development age;social coordination assessment: distinguishing between shape and timing;eye localization from infrared thermal images;the effect of fuzzy training targets on voice quality classification;a non-invasive multi-sensor capturing system for human physiological and behavioral responses analysis;motion history of skeletal volumes and temporal change in bounding volume fusion for human action recognition;multi-view multi-modal gait based human identity recognition from surveillance videos;using the transferable belief model for multimodal input fusion in companion systems;and fusion of fragmentary classifier decisions for affective state recognition.
In this paper we propose two new ensemble combiners based on the Mixture of neuralnetworks model. In our experiments, we have applied two different network architectures on the methods based on the Mixture of neural ...
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ISBN:
(纸本)9783540699385
In this paper we propose two new ensemble combiners based on the Mixture of neuralnetworks model. In our experiments, we have applied two different network architectures on the methods based on the Mixture of neuralnetworks: the Basic Network (BN) and the Multilayer Feedforward Network (MF). Moreover, we have used ensembles of MF networks previously trained with Simple Ensemble to test the performance of the combiners we propose. Finally, we compare the mixture combiners proposed with three different mixture models and other traditional combiners. The results show that the mixture combiners proposed are the best way to build Multi-net systems among the methods studied in the paper in general.
We have implemented a speech command system which can understand simple command sentences like "Bot lift ball" or "Bot go table" using hidden Markov models (HMMs) and associative memories with spar...
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ISBN:
(纸本)9783540699385
We have implemented a speech command system which can understand simple command sentences like "Bot lift ball" or "Bot go table" using hidden Markov models (HMMs) and associative memories with sparse distributed representations. The system is composed of three modules: (1) A set of HMMs is used on phoneme level to get a phonetic transcription of the spoken sentence, (2) a network of associative memories is used to determine the word belonging to the phonetic transcription and (3) a neural network is used on the sentence level to determine the meaning of the sentence. The system is also able to learn new object words during performance.
We explore the use of constraints with divisive hierarchical clustering. We mention some considerations on the effects of the inclusion of constraints into the hierarchical clustering process. Furthermore, we introduc...
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ISBN:
(纸本)3540379517
We explore the use of constraints with divisive hierarchical clustering. We mention some considerations on the effects of the inclusion of constraints into the hierarchical clustering process. Furthermore, we introduce an implementation of a semi-supervised divisive hierarchical clustering algorithm and show the influence of including constraints into the divisive hierarchical clustering process. In this task our main interest lies in building stable dendrograms when clustering with different subsets of data.
The two key factors to design an ensemble of neuralnetworks are how to train the individual networks and how to combine the different outputs to get a single output. In this paper we focus on the combination module. ...
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ISBN:
(纸本)3540379517
The two key factors to design an ensemble of neuralnetworks are how to train the individual networks and how to combine the different outputs to get a single output. In this paper we focus on the combination module. We have proposed two methods based on stacked Generalization as the combination module of an ensemble of neuralnetworks. In this paper we have performed a comparison among the two versions of stacked Generalization and six statistical combination methods in order to get the best combination method. We have used the mean increase of performance and the mean percentage or error reduction for the comparison. The results show that the methods based on stacked Generalization are better than classical combiners.
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...
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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.
Estimation of probability density functions (pdf) is one major topic in patternrecognition. Parametric techniques rely on an arbitrary assumption on the form of the underlying, unknown distribution. Nonparametric tec...
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ISBN:
(纸本)3540379517
Estimation of probability density functions (pdf) is one major topic in patternrecognition. Parametric techniques rely on an arbitrary assumption on the form of the underlying, unknown distribution. Nonparametric techniques remove this assumption In particular, the Parzen Window (PW) relies on a combination of local window functions centered in the patterns of a training sample. Although effective, PW suffers from several limitations. artificialneuralnetworks (ANN) are, in principle, an alternative family of nonparametric models. ANNs are intensively used to estimate probabilities (e.g., class-posterior probabilities), but they have not been exploited so far to estimate pdfs. This paper introduces a simple neural-based algorithm for unsupervised, nonparametric estimation of pdfs, relying on PW. The approach overcomes the limitations of PW, possibly leading to improved pdf models. An experimental demonstration of the behavior of the algorithm w.r.t. PW is presented, using random samples drawn from a standard exponential pdf.
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 F...
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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.
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