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共 274 条记录，以下是171-180 订阅

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Learning Parameters in Deep Belief networks Through Firefly Algorithm 7th

Learning Parameters in Deep Belief Networks Through Firefly ...

引用

7th iapr TC3 workshop on artificial neural networks in pattern recognition (ANNPR)

作者： Rosa, Gustavo Papa, Joao Costa, Kelton Passos, Leandro Pereira, Clayton Yang, Xin-She Sao Paulo State Univ Dept Comp Sao Paulo Brazil Univ Fed Sao Carlos Dept Comp Sao Carlos SP Brazil Middlesex Univ Sch Sci & Technol London England

ISBN: (纸本)9783319461823;9783319461816

Restricted Boltzmann Machines (RBMs) are among the most widely pursed techniques in the context of deep learning-based applications. Their usage enables sundry parallel implementations, which have become pivotal in nowadays large-scale-oriented applications. In this paper, we propose to address the main shortcoming of such models, i.e. how to properly fine-tune their parameters, by means of the Firefly Algorithm, as well as we also consider Deep Belief networks, a stackeddriven version of the RBMs. Additionally, we also take into account Harmony Search, Improved Harmony Search and the well-known Particle Swarm Optimization for comparison purposes. The results obtained showed the Firefly Algorithm is suitable to the context addressed in this paper, since it obtained the best results in all datasets.

关键词： Deep Belief networks Deep learning Firefly algorithm

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Recurrent fuzzy logic using neural network 3rd

Recurrent fuzzy logic using neural network

引用

3rd World Wisepersons workshop on Fuzzy Logic and neural networks/Genetic Algorithms, WWW 1994

作者： Khan, Emdad Unal, Fatih Intelligent Systems Group Embedded Systems Division National Semiconductor Santa ClaraCA95052 United States

ISBN: (纸本)9783540606079

In this paper, a novel method is presented to combine neural nets with fuzzy logic. The combined technology is based on modified NeuFuz ([I], [2],[3]) using recurrent neural networks. The recurrent information of neural net is directly mapped to a new type offuzzy logic, called "recurrent"fuzzy logic. Recurrencypreserves temporal information and yields superior performance for context dependent applications. It also reduces the convergence time I Simulations show good improvements in accuracy and speed of convergence in pattern recognition applications. © Springer-Verlag Berlin Heidelberg 1995.

关键词： Recurrent neural networks

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Automatic segmentation of speech at the phonetic level 9th

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Joint iapr 9th International workshop on Structural and Syntactic pattern recognition, SSPR 2002 and 4th International workshop on Statistical Techniques in pattern recognition, SPR 2002

作者： Gómez, Jon Ander Castro, María José Departamento de Sistemas Informáticos y Computación Universidad Politécnica de Valencia Valencia Spain

ISBN: (纸本)3540440119

A complete automatic speech segmentation technique has been studied in order to eliminate the need for manually segmented sentences. The goal is to fix the phoneme boundaries using only the speech waveform and the phonetic sequence of the sentences. The phonetic boundaries are established using a Dynamic Time Warping algorithm that uses the a posteriori probabilities of each phonetic unit given the acoustic frame. These a posteriori probabilities are calculated by combining the probabilities of acoustic classes which are obtained from a clustering procedure on the feature space and the conditional probabilities of each acoustic class with respect to each phonetic unit. The usefulness of the approach presented here is that manually segmented data is not needed in order to train acoustic models. The results of the obtained segmentation are similar to those obtained using the HTK toolkit with the "flat-start" option activated. Finally, results using artificial neural networks and manually segmented data are also reported for comparison purposes. © Springer-Verlag Berlin Heidelberg 2002.

关键词： neural networks

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Machine Learning for Clinical Score Prediction from Longitudinal Dataset: A Case Study on Parkinson's Disease 11th

Machine Learning for Clinical Score Prediction from Longitud...

引用

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

作者： Ahmed, Nourin Kobti, Ziad Univ Windsor Sch Comp Sci Windsor ON N9B 3P4 Canada

ISBN: (纸本)9783031716010;9783031716027

Accurate prediction of Parkinson's disease (PD) progression is vital for personalized treatment and effective clinical trials. This study presents a machine learning approach to predict the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDSUPDRS) Part III scores, quantifying motor symptom progression in PD patients. Using the longitudinal Parkinson's Progression Markers Initiative (PPMI) dataset, we examined the impact of dataset format (wide vs. cross-sectional), dimensionality reduction techniques (PCA, NMF), and regression models (Linear Regression, Random Forest, XGBoost, SVR) on prediction performance. Our findings indicate that models trained on wide-format datasets consistently outperformed those on cross-sectional data. The combination of Nonnegative Matrix Factorization (NMF) and Support Vector Regression (SVR) achieved the best performance, with a mean absolute error (MAE) of 1.91 and R-2 of 0.83. These results underscore the importance of data arrangement and highlight NMF's effectiveness in feature extraction for longitudinal datasets.

关键词： Parkinson's Disease Machine Learning Longitudinal Data Dimensionality Reduction NMF SVR Prediction Models MDS-UPDRS III

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Multimodal pattern recognition of Social Signals in Human-Computer- Interaction - First iapr TC3 workshop, MPRSS 2012, Revised Selected Papers

Multimodal Pattern Recognition of Social Signals in Human-Co...

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1st iapr TC3 workshop on Multimodal pattern recognition of Social Signals in Human-Computer-Interaction, MPRSS 2012

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.

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Vision Transformer Features-Based Leukemia Classification 11th

Vision Transformer Features-Based Leukemia Classification

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

作者： Ben-Suliman, Karima Krzyzak, Adam Concordia Univ Dept Comp Sci & Software Engn Montreal PQ H3G 1M8 Canada

ISBN: (纸本)9783031716010;9783031716027

Acute Lymphoblastic Leukemia (ALL) is a disease that is caused by the uncontrollable growth of immature and malignant White Blood Cells (WBCs) which are called lymphoblasts. It occurs when the bone marrow contains 20% or more lymphoblasts. Therefore, Leukemia is diagnosed by counting White Blood Cells (WBCs) in the microscopic smears of bone marrow and blood. There are several attempts for effective leukemia classification with the help of computer aided-based methods by analyzing microscopic smear images. However, how to further improve the classification accuracy is still challenging. Therefore, in this paper, we propose an end-to-end framework to classify ALL. First, we train a more robust specific pre-trained Vision Transformer (ViT) trained on the same type of data, ALL-IDB1. Then, we examine the extracted features of Deep ViT across layers and utilize these features as dense descriptors to make predictions. Based on the observation in this paper, we find and empirically demonstrate that such features, when extracted from the ViT model, consistently improve the classification performance. Moreover, we observe that the pre-trained ViT which is fine-tuned on specific dataset is more effective than the general images dataset. We show that our proposed method achieves an average accuracy of 98.75% which is a competitive result with recent state-of-the-art by a large margin.

关键词： Lung cancer

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Lecture Notes in artificial Intelligence: Preface

引用

Lecture Notes in Computer Science (including subseries Lecture Notes in artificial Intelligence and Lecture Notes in Bioinformatics) 2008年 5064 LNAI卷 V页

作者： Prevost, Lionel Marinai, Simone Schwenker, Friedhelm

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ISBN: (纸本)3540699384

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On instance selection in audio based emotion recognition

On instance selection in audio based emotion recognition

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

作者： Meudt, Sascha Schwenker, Friedhelm University of Ulm Institute of Neural Information Processing 89069 Ulm Germany

ISBN: (纸本)9783642332111

Affective computing aim to provide simpler and more natural interfaces for human-computer interaction applications, e.g. recognizing automatically the emotional status of the user based on facial expressions or speech is important to model user as complete as possible in order to develop human-computer interfaces that are able to respond to the user's action or behavior in an appropriate manner. In this paper we focus on audio-based emotion recognition. Data sets employed for the statistical evaluation have been collected through Wizard-of-Oz experiments. The emotional labels have been are defined through the experimental set up therefore given on a relatively coarse temporal scale (a few minutes) which This global labeling concept might lead to miss-labeled data at smaller time scales, for instance for window sizes uses in audio analysis (less than a second). Manual labeling at these time scales is very difficult not to say impossible, and therefore our approach is to use the globally defined labels in combination with instance/sample selection methods. In such an instance selection approach the task is to select the most relevant and discriminative data of the training set by using a pre-trained classifier. Mel-Frequency Cepstral Coefficients (MFCC) features are used to extract relevant features, and probabilistic support vector machines (SVM) are applied as base classifiers in our numerical evaluation. Confidence values to the samples of the training set are assigned through the outputs of the probabilistic SVM. © 2012 Springer-Verlag.

关键词： Human computer interaction

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Comparison of artificial neural networks and traditional classifiers via the two-spiral problem

Comparison of artificial neural networks and traditional cla...

引用

Proceedings of the 3rd workshop on neural networks: Academic/Industrial/NASA/Defense

作者： Suewatanakul, Witoon Himmelblau, David M. Univ of Texas at Austin Austin United States

ISBN: (纸本)1565550072

For years pattern recognition techniques have been successfully exploited in engineering to solve discrimination and classification problems. In this work, various neural network algorithms have been applied as pattern classifiers and the results of each classifier were compared with traditional methods for a difficult 2-dimensional problem so that the results could be graphed and visualized. The test problem is the problem of distinguishing between two spirals (Lang and Witbrock, 1988). The neural network classifiers involved include (1) a multilayer feedforward network with standard backpropagation, (2) a multilayer feedforward network with quickprop, (3) a network architecture with full connections between all succeeding layers proposed by Lang and Witbrock (1988), and (4) learning vector quantization (LVQ). For the traditional classifiers, both Bayesian and k-nearest neighbor classifiers were employed. The results showed that the LVQ classifier was considered to be the best classifier in the sense of processing time and performance.

关键词： pattern recognition

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Face recognition based on discriminative dictionary with multilevel feature fusion 6

引用

6th iapr TC3 International workshop on artificial neural networks for pattern recognition, ANNPR 2014

作者： Li, Hongjun Nobile, Nicola Suen, Ching Y. School of Electronic Information Engineering Nantong University Nantong226019 China Centre for Pattern Recognition and Machine Intelligence Concordia University MontrealQCH3G 1M8 Canada

ISBN: (纸本)9783319116556

In order to alleviate the influence of illumination, pose, expression and occlusion variations in face recognition, in this paper, an effective face recognition method based on discriminative sparse representation is proposed. To solve the problem of these variations, we extract discriminative features which represent for each of the training images, and propose a novel dictionary by learning discriminative features. Firstly, we decompose a test image by using nonsubsampled contourlet transform (NSCT), and then fuse the information according to the features from each subband and their contributions. Finally, we obtain the discriminative features of training images and construct a discriminative dictionary. Fuse these multiple features can improve the efficiency and effectiveness of face recognition, especially when training samples are limited and the dimension of feature vector is low. Experimental results on two widely used face databases are presented to demonstrate the efficiency of the proposed approach. © Springer International Publishing Switzerland 2014.

关键词： Face recognition

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