Data Science With Matlab Classification Techniques

DOWNLOAD

Download Data Science With Matlab Classification Techniques PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Data Science With Matlab Classification Techniques book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page

Data Science Concepts And Techniques With Applications

DOWNLOAD
Author : Usman Qamar
language : en
Publisher: Springer Nature
Release Date : 2023-04-02

Data Science Concepts And Techniques With Applications written by Usman Qamar and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-02 with Computers categories.

This textbook comprehensively covers both fundamental and advanced topics related to data science. Data science is an umbrella term that encompasses data analytics, data mining, machine learning, and several other related disciplines. The chapters of this book are organized into three parts: The first part (chapters 1 to 3) is a general introduction to data science. Starting from the basic concepts, the book will highlight the types of data, its use, its importance and issues that are normally faced in data analytics, followed by presentation of a wide range of applications and widely used techniques in data science. The second part, which has been updated and considerably extended compared to the first edition, is devoted to various techniques and tools applied in data science. Its chapters 4 to 10 detail data pre-processing, classification, clustering, text mining, deep learning, frequent pattern mining, and regression analysis. Eventually, the third part (chapters 11 and 12) present a brief introduction to Python and R, the two main data science programming languages, and shows in a completely new chapter practical data science in the WEKA (Waikato Environment for Knowledge Analysis), an open-source tool for performing different machine learning and data mining tasks. An appendix explaining the basic mathematical concepts of data science completes the book. This textbook is suitable for advanced undergraduate and graduate students as well as for industrial practitioners who carry out research in data science. They both will not only benefit from the comprehensive presentation of important topics, but also from the many application examples and the comprehensive list of further readings, which point to additional publications providing more in-depth research results or provide sources for a more detailed description of related topics. "This book delivers a systematic, carefully thoughtful material on Data Science." from the Foreword by Witold Pedrycz, U Alberta, Canada.

Data Science With Matlab Classification Techniques

DOWNLOAD
Author : A. Vidales
language : en
Publisher: Independently Published
Release Date : 2019-02-12

Data Science With Matlab Classification Techniques written by A. Vidales and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-02-12 with Mathematics categories.

Data science includes a set of statistical techniques that allow extracting the knowledge immersed in the data automatically. One of the fundamental tools in data science are classification techniques. This book develops parametric classification supervised techniques such as decision trees and discriminant analysis models. It also develops non-supervised analysis techniques such as cluster analysis.Cluster analysis, also called segmentation analysis or taxonomy analysis, creates groups, or clusters, of data. Clusters are formed in such a way that objects in the same cluster are very similar and objects in different clusters are very distinct. Measures of similarity depend on the application.Decision trees, or classification trees and regression trees, predict responses to data. To predict a response, follow the decisions in the tree from the root (beginning) node downto a leaf node. The leaf node contains the response. Classification trees give responses that are nominal, such as 'true' or 'false'. Regression trees give numeric responses. Statistics and Machine Learning Toolbox trees are binary. Each step in a prediction involves checking the value of one predictor (variable).Discriminant analysis is a classification method. It assumes that differen classes generate data based on different Gaussian distributions. To train (create) a classifier, the fitting function estimates the parameters of a Gaussian distribution for each class (see "Creating Discriminant Analysis Model" ).-To predict the classes of new data, the trained classifier find the class with the smallest misclassification cost (see "Prediction Using Discriminant Analysis Models").Linear discriminant analysis is also known as the Fisher discriminant, named for its inventor.The naive Bayes classifier is designed for use when predictors are independent of one another within each class, but it appears to work well in practice even when that independence assumption is not valid.

Advanced Soft Computing Techniques In Data Science Iot And Cloud Computing

DOWNLOAD
Author : Sujata Dash
language : en
Publisher: Springer Nature
Release Date : 2021-11-05

Advanced Soft Computing Techniques In Data Science Iot And Cloud Computing written by Sujata Dash and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-05 with Technology & Engineering categories.

This book plays a significant role in improvising human life to a great extent. The new applications of soft computing can be regarded as an emerging field in computer science, automatic control engineering, medicine, biology application, natural environmental engineering, and pattern recognition. Now, the exemplar model for soft computing is human brain. The use of various techniques of soft computing is nowadays successfully implemented in many domestic, commercial, and industrial applications due to the low-cost and very high-performance digital processors and also the decline price of the memory chips. This is the main reason behind the wider expansion of soft computing techniques and its application areas. These computing methods also play a significant role in the design and optimization in diverse engineering disciplines. With the influence and the development of the Internet of things (IoT) concept, the need for using soft computing techniques has become more significant than ever. In general, soft computing methods are closely similar to biological processes than traditional techniques, which are mostly based on formal logical systems, such as sentential logic and predicate logic, or rely heavily on computer-aided numerical analysis. Soft computing techniques are anticipated to complement each other. The aim of these techniques is to accept imprecision, uncertainties, and approximations to get a rapid solution. However, recent advancements in representation soft computing algorithms (fuzzy logic,evolutionary computation, machine learning, and probabilistic reasoning) generate a more intelligent and robust system providing a human interpretable, low-cost, approximate solution. Soft computing-based algorithms have demonstrated great performance to a variety of areas including multimedia retrieval, fault tolerance, system modelling, network architecture, Web semantics, big data analytics, time series, biomedical and health informatics, etc. Soft computing approaches such as genetic programming (GP), support vector machine–firefly algorithm (SVM-FFA), artificial neural network (ANN), and support vector machine–wavelet (SVM–Wavelet) have emerged as powerful computational models. These have also shown significant success in dealing with massive data analysis for large number of applications. All the researchers and practitioners will be highly benefited those who are working in field of computer engineering, medicine, biology application, signal processing, and mechanical engineering. This book is a good collection of state-of-the-art approaches for soft computing-based applications to various engineering fields. It is very beneficial for the new researchers and practitioners working in the field to quickly know the best performing methods. They would be able to compare different approaches and can carry forward their research in the most important area of research which has direct impact on betterment of the human life and health. This book is very useful because there is no book in the market which provides a good collection of state-of-the-art methods of soft computing-based models for multimedia retrieval, fault tolerance, system modelling, network architecture, Web semantics, big data analytics, time series, and biomedical and health informatics.

Advanced Studies In Classification And Data Science

DOWNLOAD
Author : Tadashi Imaizumi
language : en
Publisher: Springer Nature
Release Date : 2020-09-25

Advanced Studies In Classification And Data Science written by Tadashi Imaizumi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-25 with Mathematics categories.

This edited volume focuses on the latest developments in classification and data science and covers a wide range of topics in the context of data analysis and related areas, e.g. the analysis of complex data, analysis of qualitative data, methods for high-dimensional data, dimensionality reduction, data visualization, multivariate statistical methods, and various applications to real data in the social sciences, medical sciences, and other disciplines. In addition to sharing theoretical and methodological findings, the book shows how to apply the proposed methods to a variety of problems — e.g. in consumer behavior, decision-making, marketing data and social network structures. Both methodological aspects and applications to a wide range of areas such as economics, behavioral science, marketing science, management science and the social sciences are covered. The book is chiefly intended for researchers and practitioners who are interested in the latest developments and practical applications in these fields, as well as applied statisticians and data analysts. Its combination of methodological advances with a wide range of real-world applications gathered from several fields makes it of unique value in helping readers solve their research problems.

Data Science With Matlab Classification Techniques

DOWNLOAD
Author : G. Peck
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2017-11-06

Data Science With Matlab Classification Techniques written by G. Peck and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-06 with categories.

This book develops Descriptive Classification Techniques (Cluster Analysis) and Predictive Classification Techniques (Decision Trees, Discriminant Analysis and Naive bayes and Neural Networks). In addition, the book also develops Classification Learner an Neural Network Techniques. Use the Classification Learner app to train models to classify data using supervisedmachine learning. The app lets you explore supervised machine learning interactivelyusing various classifiers. Automatically train a selection of models and help you choose the best model. Modeltypes include decision trees, discriminant analysis, support vector machines, logistic regression, nearest neighbors, and ensemble classification. 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. The most important content in this book is the following: - Hierarchical Clustering - Similarity Measures - Linkages - Dendrograms - Verify the Cluster Tree - Create Clusters - k-Means Clustering - Introduction to k-Means Clustering - Create Clusters and Determine Separation - Determine the Correct Number of Clusters - Clustering Using Gaussian Mixture Models - Cluster Data from Mixture of Gaussian Distributions - Cluster Gaussian Mixture Data Using Soft Clustering - Parametric Segmentation - Evaluation Models - Performance Curves - ROC Curves - Decision Treess - Prediction Using Classification and Regression Trees - Improving Classification Trees and Regression Trees - Cross Validation - Choose Split Predictor Selection Technique - Control Depth or "Leafiness" - Pruning - Discriminant Analysis Classification - Prediction Using Discriminant Analysis Models - Confusion Matrix and cross valdation - Naive Bayes Segmentation - Data Mining and Machine Learning in MATLAB - Train Classification Models in Classification Learner App - Train Regression Models in Regression Learner App - Train Neural Networks for Deep Learning - Automated Classifier Training - Manual Classifier Training - Parallel Classifier Training - Decision Trees - Discriminant Analysis - Logistic Regression - Support Vector Machines - Nearest Neighbor Classifiers - Ensemble Classifiers - Feature Selection and Feature Transformation Using - Classification Learner App - Investigate Features in the Scatter Plot - Select Features to Include - Transform Features with PCA in Classification Learner - Investigate Features in the Parallel Coordinates Plot - Assess Classifier Performance in Classification Learner - Check Performance in the History List - Plot Classifier Results - Check the ROC Curve - Export Classification Model to Predict New Data - Export the Model to the Workspace to Make Predictions for New Data - Make Predictions for New Data - Train Decision Trees Using Classification Learner App - Train Discriminant Analysis Classifiers Using Classification Learner App - Train Logistic Regression Classifiers Using Classification Learner App - Train Support Vector Machines Using Classification Learner App - Train Nearest Neighbor Classifiers Using Classification Learner App - Train Ensemble Classifiers Using Classification Learner App - 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

Soft Computing Techniques In Vision Science

DOWNLOAD
Author : Srikanta Patnaik
language : en
Publisher: Springer
Release Date : 2012-02-10

Soft Computing Techniques In Vision Science written by Srikanta Patnaik and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-02-10 with Computers categories.

This Special Edited Volume is a unique approach towards Computational solution for the upcoming field of study called Vision Science. From a scientific firmament Optics, Ophthalmology, and Optical Science has surpassed an Odyssey of optimizing configurations of Optical systems, Surveillance Cameras and other Nano optical devices with the metaphor of Nano Science and Technology. Still these systems are falling short of its computational aspect to achieve the pinnacle of human vision system. In this edited volume much attention has been given to address the coupling issues Computational Science and Vision Studies. It is a comprehensive collection of research works addressing various related areas of Vision Science like Visual Perception and Visual system, Cognitive Psychology, Neuroscience, Psychophysics and Ophthalmology, linguistic relativity, color vision etc. This issue carries some latest developments in the form of research articles and presentations. The volume is rich of contents with technical tools for convenient experimentation in Vision Science. There are 18 research papers having significance in an array of application areas. The volume claims to be an effective compendium of computing developments like Frequent Pattern Mining, Genetic Algorithm, Gabor Filter, Support Vector Machine, Region Based Mask Filter, 4D stereo camera systems, Principal Component Analysis etc. The detailed analysis of the papers can immensely benefit to the researchers of this domain. It can be an Endeavour in the pursuit of adding value in the existing stock of knowledge in Vision Science.

Data Science And Its Applications

DOWNLOAD
Author : Aakanksha Sharaff
language : en
Publisher: CRC Press
Release Date : 2021-08-18

Data Science And Its Applications written by Aakanksha Sharaff and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-18 with Computers categories.

The term "data" being mostly used, experimented, analyzed, and researched, "Data Science and its Applications" finds relevance in all domains of research studies including science, engineering, technology, management, mathematics, and many more in wide range of applications such as sentiment analysis, social medial analytics, signal processing, gene analysis, market analysis, healthcare, bioinformatics etc. The book on Data Science and its applications discusses about data science overview, scientific methods, data processing, extraction of meaningful information from data, and insight for developing the concept from different domains, highlighting mathematical and statistical models, operations research, computer programming, machine learning, data visualization, pattern recognition and others. The book also highlights data science implementation and evaluation of performance in several emerging applications such as information retrieval, cognitive science, healthcare, and computer vision. The data analysis covers the role of data science depicting different types of data such as text, image, biomedical signal etc. useful for a wide range of real time applications. The salient features of the book are: Overview, Challenges and Opportunities in Data Science and Real Time Applications Addressing Big Data Issues Useful Machine Learning Methods Disease Detection and Healthcare Applications utilizing Data Science Concepts and Deep Learning Applications in Stock Market, Education, Behavior Analysis, Image Captioning, Gene Analysis and Scene Text Analysis Data Optimization Due to multidisciplinary applications of data science concepts, the book is intended for wide range of readers that include Data Scientists, Big Data Analysists, Research Scholars engaged in Data Science and Machine Learning applications.

Intelligent Computing And Innovation On Data Science

DOWNLOAD
Author : Sheng-Lung Peng
language : en
Publisher: Springer Nature
Release Date : 2021-09-27

Intelligent Computing And Innovation On Data Science written by Sheng-Lung Peng and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-27 with Technology & Engineering categories.

This book gathers high-quality papers presented at 2nd International Conference on Technology Innovation and Data Sciences (ICTIDS 2021), organized by Lincoln University, Malaysia from 19 – 20 February 2021. It covers wide range of recent technologies like artificial intelligence and machine learning, big data and data sciences, Internet of Things (IoT), and IoT-based digital ecosystem. The book brings together works from researchers, scientists, engineers, scholars and students in the areas of engineering and technology, and provides an opportunity for the dissemination of original research results, new ideas, research and development, practical experiments, which concentrate on both theory and practices, for the benefit of common man.

Computational Intelligence In Data Science

DOWNLOAD
Author : Mieczyslaw Lech Owoc
language : en
Publisher: Springer Nature
Release Date : 2024-08-29

Computational Intelligence In Data Science written by Mieczyslaw Lech Owoc and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-29 with Computers categories.

These two-volume set IFIP AICT 717 and 718 constitutes the refereed post-conference proceedings of the 7th International Conference on Computational Intelligence in Data Science, ICCIDS 2024, held in Chennai, India, during February 21–23, 2024. The 63 full papers and 9 short papers presented in these proceedings were carefully reviewed and selected from 259 submissions. The conference papers are organized in following topical sections: Part I: Applications of AI/ML in Natural Language Processing; and Applications of AI/ML in Image Processing. Part II: Applications of AI/ML in KDM, Cloud Computing & Security; Data Analytics; and Applications of ML.

Soft Computing For Data Analytics Classification Model And Control

DOWNLOAD
Author : Deepak Gupta
language : en
Publisher: Springer Nature
Release Date : 2022-01-30

Soft Computing For Data Analytics Classification Model And Control written by Deepak Gupta and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-30 with Technology & Engineering categories.

This book presents a set of soft computing approaches and their application in data analytics, classification model, and control. The basics of fuzzy logic implementation for advanced hybrid fuzzy driven optimization methods has been covered in the book. The various soft computing techniques, including Fuzzy Logic, Rough Sets, Neutrosophic Sets, Type-2 Fuzzy logic, Neural Networks, Generative Adversarial Networks, and Evolutionary Computation have been discussed and they are used on variety of applications including data analytics, classification model, and control. The book is divided into two thematic parts. The first thematic section covers the various soft computing approaches for text classification and data analysis, while the second section focuses on the fuzzy driven optimization methods for the control systems. The chapters has been written and edited by active researchers, which cover hypotheses and practical considerations; provide insights into the design of hybrid algorithms for applications in data analytics, classification model, and engineering control.