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New Approaches For Improving The Performance Of Neural Network Classifiers
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Book Synopsis New Approaches for Improving the Performance of Neural Network Classifiers by : Srivathsan Padmanabha-Baghavan
Download or read book New Approaches for Improving the Performance of Neural Network Classifiers written by Srivathsan Padmanabha-Baghavan and published by . This book was released on 1997 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: Radial Basis functions and Backpropagation learning algorithm are compared for speed and generalization performance of neural networks. Procedures were aided by advance mathematical and statistical concepts like Kohonen's algorithm for improving machine intelligence.
Book Synopsis Two Novel Ensemble Approaches for Improving Classification of Neural Networks by : Khobaib M. Zaamout
Download or read book Two Novel Ensemble Approaches for Improving Classification of Neural Networks written by Khobaib M. Zaamout and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The task of pattern recognition is one of the most recurrent tasks that we encounter in our lives. Therefore, there has been a significant interest of automating this task for many decades. Many techniques have been developed to this end, such as neural networks. Neural networks are excellent pattern classifiers with very robust means of learning and a relatively high classification power. Naturally, there has been an increasing interest in further improving neural networks' classification for complex problems. Many methods have been proposed. In this thesis, we propose two novel ensemble approaches to further improving neural networks' classification power, namely paralleling neural networks and chaining neural networks. The first seeks to improve a neural network's classification by combining the outputs of a set of neural networks together via another neural network. The second improves a neural network's accuracy by feeding the outputs of a neural network into another and continually doing so in a chaining fashion until the error is reduced sufficiently. The effectiveness of both approaches has been demonstrated through a series of experiments. iv.
Book Synopsis Hyperspectral Image Analysis by : Saurabh Prasad
Download or read book Hyperspectral Image Analysis written by Saurabh Prasad and published by Springer Nature. This book was released on 2020-04-27 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reviews the state of the art in algorithmic approaches addressing the practical challenges that arise with hyperspectral image analysis tasks, with a focus on emerging trends in machine learning and image processing/understanding. It presents advances in deep learning, multiple instance learning, sparse representation based learning, low-dimensional manifold models, anomalous change detection, target recognition, sensor fusion and super-resolution for robust multispectral and hyperspectral image understanding. It presents research from leading international experts who have made foundational contributions in these areas. The book covers a diverse array of applications of multispectral/hyperspectral imagery in the context of these algorithms, including remote sensing, face recognition and biomedicine. This book would be particularly beneficial to graduate students and researchers who are taking advanced courses in (or are working in) the areas of image analysis, machine learning and remote sensing with multi-channel optical imagery. Researchers and professionals in academia and industry working in areas such as electrical engineering, civil and environmental engineering, geosciences and biomedical image processing, who work with multi-channel optical data will find this book useful.
Book Synopsis A New Approach Adapting Neural Network Classifiers to Sudden Changes in Nonstationary Environments by : Alexandra Lynn Amidon
Download or read book A New Approach Adapting Neural Network Classifiers to Sudden Changes in Nonstationary Environments written by Alexandra Lynn Amidon and published by . This book was released on 2017 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Combining Artificial Neural Nets by : Amanda J.C. Sharkey
Download or read book Combining Artificial Neural Nets written by Amanda J.C. Sharkey and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume, written by leading researchers, presents methods of combining neural nets to improve their performance. The techniques include ensemble-based approaches, where a variety of methods are used to create a set of different nets trained on the same task, and modular approaches, where a task is decomposed into simpler problems. The techniques are also accompanied by an evaluation of their relative effectiveness and their application to a variety of problems.
Book Synopsis New Approaches for Multidimensional Signal Processing by : Roumen Kountchev
Download or read book New Approaches for Multidimensional Signal Processing written by Roumen Kountchev and published by Springer Nature. This book was released on 2022-12-02 with total page 287 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a collection of papers presented at the International Workshop on New Approaches for Multidimensional Signal Processing (NAMSP 2022), held at Technical University of Sofia, Sofia, Bulgaria, during 23–25 June 2022. The book covers research papers in the field of N-dimensional multicomponent image processing, multidimensional image representation and super-resolution, 3D image processing and reconstruction, MD computer vision systems, multidimensional multimedia systems, neural networks for MD image processing, data-based MD image retrieval and knowledge data mining, watermarking, hiding and encryption of MD images, MD image processing in robot systems, tensor-based data processing, 3D and multi-view visualization, forensic analysis systems for MD images and many more.
Book Synopsis Statistical and Neural Classifiers by : Sarunas Raudys
Download or read book Statistical and Neural Classifiers written by Sarunas Raudys and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 309 pages. Available in PDF, EPUB and Kindle. Book excerpt: The classification of patterns is an important area of research which is central to all pattern recognition fields, including speech, image, robotics, and data analysis. Neural networks have been used successfully in a number of these fields, but so far their application has been based on a 'black box approach' with no real understanding of how they work. In this book, Sarunas Raudys - an internationally respected researcher in the area - provides an excellent mathematical and applied introduction to how neural network classifiers work and how they should be used.. .
Book Synopsis New Classification Method Based on Modular Neural Networks with the LVQ Algorithm and Type-2 Fuzzy Logic by : Jonathan Amezcua
Download or read book New Classification Method Based on Modular Neural Networks with the LVQ Algorithm and Type-2 Fuzzy Logic written by Jonathan Amezcua and published by Springer. This book was released on 2018-02-05 with total page 78 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book a new model for data classification was developed. This new model is based on the competitive neural network Learning Vector Quantization (LVQ) and type-2 fuzzy logic. This computational model consists of the hybridization of the aforementioned techniques, using a fuzzy logic system within the competitive layer of the LVQ network to determine the shortest distance between a centroid and an input vector. This new model is based on a modular LVQ architecture to further improve its performance on complex classification problems. It also implements a data-similarity process for preprocessing the datasets, in order to build dynamic architectures, having the classes with the highest degree of similarity in different modules. Some architectures were developed in order to work mainly with two datasets, an arrhythmia dataset (using ECG signals) for classifying 15 different types of arrhythmias, and a satellite images segments dataset used for classifying six different types of soil. Both datasets show interesting features that makes them interesting for testing new classification methods.
Book Synopsis Intelligent Techniques in Recommendation Systems: Contextual Advancements and New Methods by : Dehuri, Satchidananda
Download or read book Intelligent Techniques in Recommendation Systems: Contextual Advancements and New Methods written by Dehuri, Satchidananda and published by IGI Global. This book was released on 2012-11-30 with total page 351 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although recommendation systems have become a vital research area in the fields of cognitive science, approximation theory, information retrieval and management sciences, they still require improvements to make recommendation methods more effective and intelligent. Intelligent Techniques in Recommendation Systems: Contextual Advancements and New Methods is a comprehensive collection of research on the latest advancements of intelligence techniques and their application to recommendation systems and how this could improve this field of study.
Book Synopsis Support Vector Machines for Pattern Classification by : Shigeo Abe
Download or read book Support Vector Machines for Pattern Classification written by Shigeo Abe and published by Springer Science & Business Media. This book was released on 2010-07-23 with total page 486 pages. Available in PDF, EPUB and Kindle. Book excerpt: A guide on the use of SVMs in pattern classification, including a rigorous performance comparison of classifiers and regressors. The book presents architectures for multiclass classification and function approximation problems, as well as evaluation criteria for classifiers and regressors. Features: Clarifies the characteristics of two-class SVMs; Discusses kernel methods for improving the generalization ability of neural networks and fuzzy systems; Contains ample illustrations and examples; Includes performance evaluation using publicly available data sets; Examines Mahalanobis kernels, empirical feature space, and the effect of model selection by cross-validation; Covers sparse SVMs, learning using privileged information, semi-supervised learning, multiple classifier systems, and multiple kernel learning; Explores incremental training based batch training and active-set training methods, and decomposition techniques for linear programming SVMs; Discusses variable selection for support vector regressors.
Book Synopsis Neural Networks and Statistical Learning by : Ke-Lin Du
Download or read book Neural Networks and Statistical Learning written by Ke-Lin Du and published by Springer Nature. This book was released on 2019-09-12 with total page 988 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a broad yet detailed introduction to neural networks and machine learning in a statistical framework. A single, comprehensive resource for study and further research, it explores the major popular neural network models and statistical learning approaches with examples and exercises and allows readers to gain a practical working understanding of the content. This updated new edition presents recently published results and includes six new chapters that correspond to the recent advances in computational learning theory, sparse coding, deep learning, big data and cloud computing. Each chapter features state-of-the-art descriptions and significant research findings. The topics covered include: • multilayer perceptron; • the Hopfield network; • associative memory models;• clustering models and algorithms; • t he radial basis function network; • recurrent neural networks; • nonnegative matrix factorization; • independent component analysis; •probabilistic and Bayesian networks; and • fuzzy sets and logic. Focusing on the prominent accomplishments and their practical aspects, this book provides academic and technical staff, as well as graduate students and researchers with a solid foundation and comprehensive reference on the fields of neural networks, pattern recognition, signal processing, and machine learning.
Book Synopsis Multi-faceted Deep Learning by : Jenny Benois-Pineau
Download or read book Multi-faceted Deep Learning written by Jenny Benois-Pineau and published by Springer Nature. This book was released on 2021-10-20 with total page 321 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers a large set of methods in the field of Artificial Intelligence - Deep Learning applied to real-world problems. The fundamentals of the Deep Learning approach and different types of Deep Neural Networks (DNNs) are first summarized in this book, which offers a comprehensive preamble for further problem–oriented chapters. The most interesting and open problems of machine learning in the framework of Deep Learning are discussed in this book and solutions are proposed. This book illustrates how to implement the zero-shot learning with Deep Neural Network Classifiers, which require a large amount of training data. The lack of annotated training data naturally pushes the researchers to implement low supervision algorithms. Metric learning is a long-term research but in the framework of Deep Learning approaches, it gets freshness and originality. Fine-grained classification with a low inter-class variability is a difficult problem for any classification tasks. This book presents how it is solved, by using different modalities and attention mechanisms in 3D convolutional networks. Researchers focused on Machine Learning, Deep learning, Multimedia and Computer Vision will want to buy this book. Advanced level students studying computer science within these topic areas will also find this book useful.
Book Synopsis Proceedings of the First International Conference on Advanced Data and Information Engineering (DaEng-2013) by : Tutut Herawan
Download or read book Proceedings of the First International Conference on Advanced Data and Information Engineering (DaEng-2013) written by Tutut Herawan and published by Springer Science & Business Media. This book was released on 2013-12-14 with total page 728 pages. Available in PDF, EPUB and Kindle. Book excerpt: The proceeding is a collection of research papers presented at the International Conference on Data Engineering 2013 (DaEng-2013), a conference dedicated to address the challenges in the areas of database, information retrieval, data mining and knowledge management, thereby presenting a consolidated view to the interested researchers in the aforesaid fields. The goal of this conference was to bring together researchers and practitioners from academia and industry to focus on advanced on data engineering concepts and establishing new collaborations in these areas. The topics of interest are as follows but are not limited to: • Database theory • Data management • Data mining and warehousing • Data privacy & security • Information retrieval, integration and visualization • Information system • Knowledge discovery in databases • Mobile, grid and cloud computing • Knowledge-based • Knowledge management • Web data, services and intelligence
Book Synopsis New Approaches to Solve the Local Minimum Problem and Improve the Classification Ability of Learning Algorithms in Multi-layer Feed-forward Neural Networks by : Shensheng Xu
Download or read book New Approaches to Solve the Local Minimum Problem and Improve the Classification Ability of Learning Algorithms in Multi-layer Feed-forward Neural Networks written by Shensheng Xu and published by . This book was released on 2016 with total page 78 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Improving the Performance of Ensemble Classifier Models Through the Local Specialization of Base Classifiers by : Brian Michael Ironside
Download or read book Improving the Performance of Ensemble Classifier Models Through the Local Specialization of Base Classifiers written by Brian Michael Ironside and published by . This book was released on 2016 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: Two approaches are tested to improve the predictive performance of classification ensembles, individually, and in combination. Eight datasets from the UCI machine learning repository are used to test the efficacy of these approaches. To reflect patterns specific to subregions of the data, base classifiers are trained on partitions of the training data generated by an unsupervised model. Gaussian modelbased clustering is used to create unsupervised partitions, due to its ability to model differences in the variances of predictors, correlations between predictors, and provide estimates of cluster membership probability for each data point. These clusters are evaluated according to their separation and robustness to initial conditions and data perturbations. The hypothesis space available to the classification ensembles is expanded by the use of multiple classification algorithms to train base classifiers. Classification trees, knearest neighbors, linear discriminant analysis, and artificial neural networks are used to train base classifiers, and provide a diverse set of model generation and pattern summarization heuristics. Ensembles are constructed using an “over-produce and select” strategy. A library of base classifiers are created, and then a subset is selected for inclusion in the ensemble in a stepwise manner, according to their ability to improve the mean squared error of the ensemble. The library of classifiers contains models trained on unsupervised partitions of the data, or trained using multiple algorithms, or both. To reflect both the refinement and calibration of the base classifiers when producing the ensemble prediction, predictions are weighted by their individual mean squared error. The ensembles strategies outperform the base classifiers. The ensembles composed of classifiers trained on unsupervised partitions of the data, but using only a single classification algorithm to train the base classifiers, outperform the ensembles created using multiple classification algorithms.
Book Synopsis Modular Learning in Neural Networks by : Tomas Hrycej
Download or read book Modular Learning in Neural Networks written by Tomas Hrycej and published by Wiley-Interscience. This book was released on 1992-10-09 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Modular Learning in Neural Networks covers the full range of conceivable approaches to the modularization of learning, including decomposition of learning into modules using supervised and unsupervised learning types; decomposition of the function to be mapped into linear and nonlinear parts; decomposition of the neural network to minimize harmful interferences between a large number of network parameters during learning; decomposition of the application task into subtasks that are learned separately; decomposition into a knowledge-based part and a learning part. The book attempts to show that modular learning based on these approaches is helpful in improving the learning performance of neural networks. It demonstrates this by applying modular methods to a pair of benchmark cases - a medical classification problem of realistic size, encompassing 7,200 cases of thyroid disorder; and a handwritten digits classification problem, involving several thousand cases. In so doing, the book shows that some of the proposed methods lead to substantial improvements in solution quality and learning speed, as well as enhanced robustness with regard to learning control parameters.".
Book Synopsis Advances in Neural Networks--ISNN 2004 by :
Download or read book Advances in Neural Networks--ISNN 2004 written by and published by . This book was released on 2004 with total page 2062 pages. Available in PDF, EPUB and Kindle. Book excerpt:
