Neural Networks Using Matlab. Cluster Analysis and Classification PDF Download
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Author: K. Taylor
Publisher: Createspace Independent Publishing Platform
ISBN: 9781543172584
Category :
Languages : en
Pages : 396
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Book Description
MATLAB has the tool Neural Network Toolbox that provides algorithms, functions, and apps to create, train, visualize, and simulate neural networks. You can perform classification, regression, clustering, dimensionality reduction, time-series forecasting, and dynamic system modeling and control. The toolbox includes convolutional neural network and autoencoder deep learning algorithms for image classification and feature learning tasks. To speed up training of large data sets, you can distribute computations and data across multicore processors, GPUs, and computer clusters using Parallel Computing Toolbox. The more important features are the following: -Deep learning, including convolutional neural networks and autoencoders -Parallel computing and GPU support for accelerating training (with Parallel Computing Toolbox) -Supervised learning algorithms, including multilayer, radial basis, learning vector quantization (LVQ), time-delay, nonlinear autoregressive (NARX), and recurrent neural network (RNN) -Unsupervised learning algorithms, including self-organizing maps and competitive layers -Apps for data-fitting, pattern recognition, and clustering -Preprocessing, postprocessing, and network visualization for improving training efficiency and assessing network performance -Simulink(R) blocks for building and evaluating neural networks and for control systems applications This book develops cluster analysis and classification tecniques using neural networks
Author: Perez Lopez Cesar Perez Lopez
Publisher:
ISBN: 9781678018672
Category :
Languages : en
Pages : 0
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Book Description
Author: K. Taylor
Publisher: Createspace Independent Publishing Platform
ISBN: 9781543172584
Category :
Languages : en
Pages : 396
Get Book Here
Book Description
MATLAB has the tool Neural Network Toolbox that provides algorithms, functions, and apps to create, train, visualize, and simulate neural networks. You can perform classification, regression, clustering, dimensionality reduction, time-series forecasting, and dynamic system modeling and control. The toolbox includes convolutional neural network and autoencoder deep learning algorithms for image classification and feature learning tasks. To speed up training of large data sets, you can distribute computations and data across multicore processors, GPUs, and computer clusters using Parallel Computing Toolbox. The more important features are the following: -Deep learning, including convolutional neural networks and autoencoders -Parallel computing and GPU support for accelerating training (with Parallel Computing Toolbox) -Supervised learning algorithms, including multilayer, radial basis, learning vector quantization (LVQ), time-delay, nonlinear autoregressive (NARX), and recurrent neural network (RNN) -Unsupervised learning algorithms, including self-organizing maps and competitive layers -Apps for data-fitting, pattern recognition, and clustering -Preprocessing, postprocessing, and network visualization for improving training efficiency and assessing network performance -Simulink(R) blocks for building and evaluating neural networks and for control systems applications This book develops cluster analysis and classification tecniques using neural networks
Author: C. Perez
Publisher: CESAR PEREZ
ISBN: 1716876869
Category : Computers
Languages : en
Pages : 389
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Book Description
Big Data Analytics examines large amounts of data to uncover hidden patterns, correlations and other insights. MATLAB has the tool Neural Network Toolbox (Deep Learning Toolbox from version 18) that provides algorithms, functions, and apps to create, train, visualize, and simulate neural networks. You can perform classification, regression, clustering, dimensionality reduction, time-series forecasting, and dynamic system modeling and control.The toolbox includes convolutional neural network and autoencoder deep learning algorithms for image classification and feature learning tasks. To speed up training of large data sets, you can distribute computations and data across multicore processors, GPUs, and computer clusters using Big Data tools (Parallel Computing Toolbox). Unsupervised learning algorithms, including self-organizing maps and competitive layers-Apps for data-fitting, pattern recognition, and clustering-Preprocessing, postprocessing, and network visualization for improving training efficiency and assessing network performance. his book develops cluster analysis and pattern recognition
Author: C Perez
Publisher: Independently Published
ISBN: 9781096862611
Category :
Languages : en
Pages : 218
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Book Description
MATLAB has the tool Neural Network Toolbox (Deep Learning Toolbox from version 18) that provides algorithms, functions, and apps to create, train, visualize, and simulate neural networks. You can perform classification, regression, clustering, dimensionality reduction, time-series forecasting, and dynamic system modeling and control.The toolbox includes convolutional neural network and autoencoder deep learning algorithms for image classification and feature learning tasks. To speed up training of large data sets, you can distribute computations and data across multicore processors, GPUs, and computer clusters using Parallel Computing Toolbox.This book develops statistics and data analysis methods for cluster analysis and pattern recognition with neural networks using MATLAB. the most important topics are the next: CLUSTER DATA WITH NEURAL NETWORKSCLUSTER WITH SELF-ORGANIZING MAP NEURAL NETWORKSELF-ORGANIZING MAPS. FUNCTIONSCOMPETITIVE NEURAL NETWORKSCOMPETITITVE LAYERSCLASSIFY PATTERNS WITH A NEURAL NETWORKFUNCTIONS FOR PATTERN RECOGNITION AND CLASSIFICATIONCLASSIFICATION WITH NEURAL NETWORKS. EXAMPLE
Author: G. Peck
Publisher: Createspace Independent Publishing Platform
ISBN: 9781979518987
Category :
Languages : en
Pages : 184
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Book Description
Cluster analysis, also called segmentation analysis or taxonomy analysis, partitions sample data into groups or clusters. Clusters are formed such that objects in the same cluster are very similar, and objects in different clusters are very distinct. Statistics and Machine Learning Toolbox provides several clustering techniques and measures of similarity (also called distance measures) to create the clusters. Additionally, cluster evaluation determines the optimal number of clusters for the data using different evaluation criteria. Cluster visualization options include dendrograms and silhouette plots. "Hierarchical Clustering" groups data over a variety of scales by creating a cluster tree or dendrogram. The tree is not a single set of clusters, but rather a multilevel hierarchy, where clusters at one level are joined as clusters at the next level. This allows you to decide the level or scale of clustering that is most appropriate for your application. The Statistics and Machine Learning Toolbox function clusterdata performs all of the necessary steps for you. It incorporates the pdist, linkage, and cluster functions, which may be used separately for more detailed analysis. The dendrogram function plots the cluster tree. "k-Means Clustering" is a partitioning method. The function kmeans partitions data into k mutually exclusive clusters, and returns the index of the cluster to which it has assigned each observation. Unlike hierarchical clustering, k-means clustering operates on actual observations (rather than the larger set of dissimilarity measures), and creates a single level of clusters. The distinctions mean that k-means clustering is often more suitable than hierarchical clustering for large amounts of data. "Clustering Using Gaussian Mixture Models" form clusters by representing the probability density function of observed variables as a mixture of multivariate normal densities. Mixture models of the gmdistribution class use an expectation maximization (EM) algorithm to fit data, which assigns posterior probabilities to each component density with respect to each observation. Clusters are assigned by selecting the component that maximizes the posterior probability. Clustering using Gaussian mixture models is sometimes considered a soft clustering method. The posterior probabilities for each point indicate that each data point has some probability of belonging to each cluster. Like k-means clustering, Gaussian mixture modeling uses an iterative algorithm that converges to a local optimum. Gaussian mixture modeling may be more appropriate than k-means clustering when clusters have different sizes and correlation within them. Neural Network Toolbox provides algorithms, pretrained models, and apps to create, train, visualize, and simulate both shallow and deep neural networks. You can perform classification, regression, clustering, dimensionality reduction, time-series forecasting, and dynamic system modeling and control. This book develops Cluster Techniques: Hierarchical Clustering, k-Means Clustering, Clustering Using Gaussian Mixture Models and Clustering using Neural Networks. The most important content in this book is the following: - Hierarchical Clustering - Algorithm Description - Similarity Measures - Linkages - Dendrograms - Verify the Cluster Tree - Create Clusters - k-Means Clustering - Create Clusters and Determine Separation - Determine the Correct Number of Clusters - Avoid Local Minima - Clustering Using Gaussian Mixture Models - Cluster Data from Mixture of Gaussian Distributions - Cluster Gaussian Mixture Data Using Soft Clustering - Tune Gaussian Mixture Models - Shallow Networks for Pattern Recognition, Clustering and Time Series - Fit Data with a Shallow Neural Network - Classify Patterns with a Shallow Neural Network - Cluster Data with a Self-Organizing Map - Shallow Neural Network Time-Series Prediction and Modeling
Author: K. Taylor
Publisher: Createspace Independent Publishing Platform
ISBN: 9781545247303
Category :
Languages : en
Pages : 416
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Book Description
Cluster analisys is a set of unsupervised learning techniques to find natural groupings and patterns in data. Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, bioinformatics, data compression, and computer graphics. Cluster analysis, also called segmentation analysis or taxonomy analysis, partitions sample data into groups or clusters. Clusters are formed such that objects in the same cluster are very similar, and objects in different clusters are very distinct. MATLAB Statistics and Machine Learning Toolbox provides several clustering techniques and measures of similarity (also called distance measures) to create the clusters. Additionally, cluster evaluation determines the optimal number of clusters for the data using different evaluation criteria. Cluster visualization options include dendrograms and silhouette plots. Besides the term clustering, there are a number of terms with similar meanings, including automatic classification, numerical taxonomy, and typological analysis. The subtle differences are often in the usage of the results: while in data mining, the resulting groups are the matter of interest, in automatic classification the resulting discriminative power is of interest. The more important topics in this book are de following: Cluster analisys. Hierarchical clustering Cluster analisys. Non hierarchical clustering Cluster analisys. Gaussian mixture models and hidden markov models Cluster analisys. Nearest neighbors. KNN classifiers Cluster visualization and evaluation Cluster data with neural networks Cluster with self-organizing map neural network Self-organizing maps. Functions Competitive neural networks Competitive layers Classify patterns with a neural network Functions for pattern recognition and classification Classification with neural networks. Examples Autoencoders and clustering with neural networks. Examples Self-organizing networks. Examples
Author: C. Perez
Publisher: CESAR PEREZ
ISBN: 1716877423
Category : Computers
Languages : en
Pages : 322
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Book Description
Big data analytics is the process of collecting, organizing and analyzing large sets of data (called big data) to discover patterns and other useful information. Big data analytics can help organizations to better understand the information contained within the data and will also help identify the data that is most important to the business and future business decisions. Analysts working with big data basically want the knowledge that comes from analyzing the data.To analyze such a large volume of data, big data analytics is typically performed using specialized software tools and applications for predictive analytics, data mining, text mining, forecasting and data optimization. Collectively these processes are separate but highly integrated functions of high-performance analytics. Using big data tools and software enables an organization to process extremely large volumes of data that a business has collected to determine which data is relevant and can be analyzed to drive better business decisions in the future. Among all these tools highlights MATLAB. MATLAB implements various toolboxes for working on big data analytics, such as Statistics Toolbox and Neural Network Toolbox (Deep Learning Toolbox for version 18) . This book develops the work capabilities of MATLAB with Neural Networks and Big Data.
Author: C. Perez
Publisher: Independently Published
ISBN: 9781091082502
Category : Computers
Languages : en
Pages : 172
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Book Description
MATLAB has the tool Neural Network Toolbox or Deep Learning Tools that provides algorithms, functions, and apps to create, train, visualize, and simulate neural networks. You can perform classification, regression, clustering, dimensionality reduction, time-series forecasting, and dynamic system modeling and control.The toolbox includes convolutional neural network and autoencoder deep learning algorithms for image classification and feature learning tasks. To speed up training of large data sets, you can distribute computations and data across multicore processors, GPUs, and computer clusters using Parallel Computing Toolbox.The more important features are the following: -Deep learning, including convolutional neural networks and autoencoders-Parallel computing and GPU support for accelerating training (with Parallel Computing Toolbox) -Supervised learning algorithms, including multilayer, radial basis, learning vector quantization (LVQ), time-delay, nonlinear autoregressive (NARX), and recurrent neural network (RNN)-Unsupervised learning algorithms, including self-organizing maps and competitive layers-Apps for data-fitting, pattern recognition, and clustering
Author: Giuseppe Ciaburro
Publisher: Packt Publishing Ltd
ISBN: 1788399390
Category : Computers
Languages : en
Pages : 374
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Book Description
Extract patterns and knowledge from your data in easy way using MATLAB About This Book Get your first steps into machine learning with the help of this easy-to-follow guide Learn regression, clustering, classification, predictive analytics, artificial neural networks and more with MATLAB Understand how your data works and identify hidden layers in the data with the power of machine learning. Who This Book Is For This book is for data analysts, data scientists, students, or anyone who is looking to get started with machine learning and want to build efficient data processing and predicting applications. A mathematical and statistical background will really help in following this book well. What You Will Learn Learn the introductory concepts of machine learning. Discover different ways to transform data using SAS XPORT, import and export tools, Explore the different types of regression techniques such as simple & multiple linear regression, ordinary least squares estimation, correlations and how to apply them to your data. Discover the basics of classification methods and how to implement Naive Bayes algorithm and Decision Trees in the Matlab environment. Uncover how to use clustering methods like hierarchical clustering to grouping data using the similarity measures. Know how to perform data fitting, pattern recognition, and clustering analysis with the help of MATLAB Neural Network Toolbox. Learn feature selection and extraction for dimensionality reduction leading to improved performance. In Detail MATLAB is the language of choice for many researchers and mathematics experts for machine learning. This book will help you build a foundation in machine learning using MATLAB for beginners. You'll start by getting your system ready with t he MATLAB environment for machine learning and you'll see how to easily interact with the Matlab workspace. We'll then move on to data cleansing, mining and analyzing various data types in machine learning and you'll see how to display data values on a plot. Next, you'll get to know about the different types of regression techniques and how to apply them to your data using the MATLAB functions. You'll understand the basic concepts of neural networks and perform data fitting, pattern recognition, and clustering analysis. Finally, you'll explore feature selection and extraction techniques for dimensionality reduction for performance improvement. At the end of the book, you will learn to put it all together into real-world cases covering major machine learning algorithms and be comfortable in performing machine learning with MATLAB. Style and approach The book takes a very comprehensive approach to enhance your understanding of machine learning using MATLAB. Sufficient real-world examples and use cases are included in the book to help you grasp the concepts quickly and apply them easily in your day-to-day work.
Author: Brunello Tirozzi
Publisher: World Scientific
ISBN: 9812771271
Category : Medical
Languages : en
Pages : 242
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Book Description
This volume provides students with the necessary tools to better understand the fields of neurobiological modeling, cluster analysis of proteins and genes. The theory is explained starting from the beginning and in the most elementary terms, there are many exercises solved and not useful for the understanding of the theory. The exercises are specially adapted for training and many useful Matlab programs are included, easily understood and generalizable to more complex situations. This self-contained text is particularly suitable for an undergraduate course of biology and biotechnology. New results are also provided for researchers such as the description and applications of the Kohonen neural networks to gene classification and protein classification with back propagation neutral networks.
