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From Text Classification To Image Clustering Problems Less Optimized
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Book Synopsis From Text Classification to Image Clustering, Problems Less Optimized by : Amirhossein Herandi
Download or read book From Text Classification to Image Clustering, Problems Less Optimized written by Amirhossein Herandi and published by . This book was released on 2019 with total page 54 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning is thriving. Every industry is using its techniques in some way to improve their efficiency and revenue. However, the focus on research is not divided equally between all of the different areas and problems that this field can tackle and analyze. Currently, Computer Vision is the one area that is being focused very extensively by researchers and companies alike, and as a result has seen an amazing boost in the recent years. This ranges from the well-known problems of classification that use discriminative models all the way to more novel problems that use generative models such as style transfer, super resolution, and description generation. Yet, some other problems have not been worked on nearly as much as of now. These problems include some Natural Language Processing tasks like Sentence Classification and even Computer Vision problems such as Image Clustering. Each of these tasks has their own set of difficulties and obstructions that need to be tackled before they can be researched properly and used in the industry which is a great driving force for research. Specifically, the case of clustering seems to be interesting to look into as more and more lable-less and unknown data is being generated every day without means to process and analyze them efficiently. We will discuss these problems that have been focused on less throughout the recent years.
Book Synopsis Image Clustering and Classification Using Optimization Algorithms by : Praveena Segu
Download or read book Image Clustering and Classification Using Optimization Algorithms written by Praveena Segu and published by . This book was released on 2016-08-05 with total page 116 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Unsupervised Classification by : Sanghamitra Bandyopadhyay
Download or read book Unsupervised Classification written by Sanghamitra Bandyopadhyay and published by Springer. This book was released on 2012-12-12 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: Clustering is an important unsupervised classification technique where data points are grouped such that points that are similar in some sense belong to the same cluster. Cluster analysis is a complex problem as a variety of similarity and dissimilarity measures exist in the literature. This is the first book focused on clustering with a particular emphasis on symmetry-based measures of similarity and metaheuristic approaches. The aim is to find a suitable grouping of the input data set so that some criteria are optimized, and using this the authors frame the clustering problem as an optimization one where the objectives to be optimized may represent different characteristics such as compactness, symmetrical compactness, separation between clusters, or connectivity within a cluster. They explain the techniques in detail and outline many detailed applications in data mining, remote sensing and brain imaging, gene expression data analysis, and face detection. The book will be useful to graduate students and researchers in computer science, electrical engineering, system science, and information technology, both as a text and as a reference book. It will also be useful to researchers and practitioners in industry working on pattern recognition, data mining, soft computing, metaheuristics, bioinformatics, remote sensing, and brain imaging.
Book Synopsis Predictive Clustering by : Hendrik Blockeel
Download or read book Predictive Clustering written by Hendrik Blockeel and published by Springer. This book was released on 2012-05-31 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces a novel paradigm for machine learning and data mining called predictive clustering, which covers a broad variety of learning tasks and offers a fresh perspective on existing techniques. The book presents an informal introduction to predictive clustering, describing learning tasks and settings, and then continues with a formal description of the paradigm, explaining algorithms for learning predictive clustering trees and predictive clustering rules, as well as presenting the applicability of these learning techniques to a broad range of tasks. Variants of decision tree learning algorithms are also introduced. Finally, the book offers several significant applications in ecology and bio-informatics. The book is written in a straightforward and easy-to-understand manner, aimed at varied readership, ranging from researchers with an interest in machine learning techniques to practitioners of data mining technology in the areas of ecology and bioinformatics.
Book Synopsis Partitional Clustering via Nonsmooth Optimization by : Adil M. Bagirov
Download or read book Partitional Clustering via Nonsmooth Optimization written by Adil M. Bagirov and published by Springer Nature. This book was released on 2020-02-24 with total page 343 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes optimization models of clustering problems and clustering algorithms based on optimization techniques, including their implementation, evaluation, and applications. The book gives a comprehensive and detailed description of optimization approaches for solving clustering problems; the authors' emphasis on clustering algorithms is based on deterministic methods of optimization. The book also includes results on real-time clustering algorithms based on optimization techniques, addresses implementation issues of these clustering algorithms, and discusses new challenges arising from big data. The book is ideal for anyone teaching or learning clustering algorithms. It provides an accessible introduction to the field and it is well suited for practitioners already familiar with the basics of optimization.
Author :Christodoulos A. Floudas Publisher :Springer Science & Business Media ISBN 13 :0387747583 Total Pages :4646 pages Book Rating :4.3/5 (877 download)
Book Synopsis Encyclopedia of Optimization by : Christodoulos A. Floudas
Download or read book Encyclopedia of Optimization written by Christodoulos A. Floudas and published by Springer Science & Business Media. This book was released on 2008-09-04 with total page 4646 pages. Available in PDF, EPUB and Kindle. Book excerpt: The goal of the Encyclopedia of Optimization is to introduce the reader to a complete set of topics that show the spectrum of research, the richness of ideas, and the breadth of applications that has come from this field. The second edition builds on the success of the former edition with more than 150 completely new entries, designed to ensure that the reference addresses recent areas where optimization theories and techniques have advanced. Particularly heavy attention resulted in health science and transportation, with entries such as "Algorithms for Genomics", "Optimization and Radiotherapy Treatment Design", and "Crew Scheduling".
Book Synopsis An efficient classification framework for breast cancer using hyper parameter tuned Random Decision Forest Classifier and Bayesian Optimization by : Pratheep Kumar
Download or read book An efficient classification framework for breast cancer using hyper parameter tuned Random Decision Forest Classifier and Bayesian Optimization written by Pratheep Kumar and published by Infinite Study. This book was released on with total page 11 pages. Available in PDF, EPUB and Kindle. Book excerpt: Decision tree algorithm is one of the algorithm which is easily understandable and interpretable algorithm used in both training and application purpose during breast cancer prognosis. To address this problem, Random Decision Forests are proposed. In this manuscript, the breast cancer classification can be determined by combining the advantages of Feature Weight and Hyper Parameter Tuned Random Decision Forest classifier
Book Synopsis Metaheuristics for Data Clustering and Image Segmentation by : Meera Ramadas
Download or read book Metaheuristics for Data Clustering and Image Segmentation written by Meera Ramadas and published by Springer. This book was released on 2018-12-12 with total page 163 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book, differential evolution and its modified variants are applied to the clustering of data and images. Metaheuristics have emerged as potential algorithms for dealing with complex optimization problems, which are otherwise difficult to solve using traditional methods. In this regard, differential evolution is considered to be a highly promising technique for optimization and is being used to solve various real-time problems. The book studies the algorithms in detail, tests them on a range of test images, and carefully analyzes their performance. Accordingly, it offers a valuable reference guide for all researchers, students and practitioners working in the fields of artificial intelligence, optimization and data analytics.
Book Synopsis Handbook of Research on Big Data Clustering and Machine Learning by : Garcia Marquez, Fausto Pedro
Download or read book Handbook of Research on Big Data Clustering and Machine Learning written by Garcia Marquez, Fausto Pedro and published by IGI Global. This book was released on 2019-10-04 with total page 478 pages. Available in PDF, EPUB and Kindle. Book excerpt: As organizations continue to develop, there is an increasing need for technological methods that can keep up with the rising amount of data and information that is being generated. Machine learning is a tool that has become powerful due to its ability to analyze large amounts of data quickly. Machine learning is one of many technological advancements that is being implemented into a multitude of specialized fields. An extensive study on the execution of these advancements within professional industries is necessary. The Handbook of Research on Big Data Clustering and Machine Learning is an essential reference source that synthesizes the analytic principles of clustering and machine learning to big data and provides an interface between the main disciplines of engineering/technology and the organizational, administrative, and planning abilities of management. Featuring research on topics such as project management, contextual data modeling, and business information systems, this book is ideally designed for engineers, economists, finance officers, marketers, decision makers, business professionals, industry practitioners, academicians, students, and researchers seeking coverage on the implementation of big data and machine learning within specific professional fields.
Book Synopsis Optimization, Simulation and Control by : Rentsen Enkhbat
Download or read book Optimization, Simulation and Control written by Rentsen Enkhbat and published by Springer Nature. This book was released on 2023-12-01 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume gathers selected, peer-reviewed works presented at the 7th International Conference on Optimization, Simulation and Control, ICOSC 2022, held at the National University of Mongolia, Ulaanbaatar, June 20–22, 2022. Topics covered include (but are not limited to) mathematical programming; network, global, linear, nonlinear, parametric, stochastic, and multi-objective optimization; control theory; biomathematics; and deep and machine learning, to name a few. Held every three years since 2002, the ICOSC conference has become a traditional gathering for experienced and young researchers in optimization and control to share recent findings in these fields and discuss novel applications in myriad sectors. Researchers and graduate students in the fields of mathematics, engineering, and computer science can greatly benefit from this book, which can also be enjoyed by advanced practitioners in research laboratories and the industry. The 2022 edition of the ICOSC conference was sponsored by the Mongolian Academy of Sciences, the National University of Mongolia and the German-Mongolian Institute for Resources and Technology.
Book Synopsis Optimization for Decision Making II by : Víctor Yepes
Download or read book Optimization for Decision Making II written by Víctor Yepes and published by MDPI. This book was released on 2020-11-25 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the current context of the electronic governance of society, both administrations and citizens are demanding the greater participation of all the actors involved in the decision-making process relative to the governance of society. This book presents collective works published in the recent Special Issue (SI) entitled “Optimization for Decision Making II”. These works give an appropriate response to the new challenges raised, the decision-making process can be done by applying different methods and tools, as well as using different objectives. In real-life problems, the formulation of decision-making problems and the application of optimization techniques to support decisions are particularly complex and a wide range of optimization techniques and methodologies are used to minimize risks, improve quality in making decisions or, in general, to solve problems. In addition, a sensitivity or robustness analysis should be done to validate/analyze the influence of uncertainty regarding decision-making. This book brings together a collection of inter-/multi-disciplinary works applied to the optimization of decision making in a coherent manner.
Book Synopsis Evolutionary Computation by : David B. Fogel
Download or read book Evolutionary Computation written by David B. Fogel and published by John Wiley & Sons. This book was released on 2006-01-03 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Third Edition provides the latest tools and techniques that enable computers to learn The Third Edition of this internationally acclaimed publication provides the latest theory and techniques for using simulated evolution to achieve machine intelligence. As a leading advocate for evolutionary computation, the author has successfully challenged the traditional notion of artificial intelligence, which essentially programs human knowledge fact by fact, but does not have the capacity to learn or adapt as evolutionary computation does. Readers gain an understanding of the history of evolutionary computation, which provides a foundation for the author's thorough presentation of the latest theories shaping current research. Balancing theory with practice, the author provides readers with the skills they need to apply evolutionary algorithms that can solve many of today's intransigent problems by adapting to new challenges and learning from experience. Several examples are provided that demonstrate how these evolutionary algorithms learn to solve problems. In particular, the author provides a detailed example of how an algorithm is used to evolve strategies for playing chess and checkers. As readers progress through the publication, they gain an increasing appreciation and understanding of the relationship between learning and intelligence. Readers familiar with the previous editions will discover much new and revised material that brings the publication thoroughly up to date with the latest research, including the latest theories and empirical properties of evolutionary computation. The Third Edition also features new knowledge-building aids. Readers will find a host of new and revised examples. New questions at the end of each chapter enable readers to test their knowledge. Intriguing assignments that prepare readers to manage challenges in industry and research have been added to the end of each chapter as well. This is a must-have reference for professionals in computer and electrical engineering; it provides them with the very latest techniques and applications in machine intelligence. With its question sets and assignments, the publication is also recommended as a graduate-level textbook.
Book Synopsis Text Classification Aided by Clustering: a Literature Review by : Antonia Kyriakopoulou
Download or read book Text Classification Aided by Clustering: a Literature Review written by Antonia Kyriakopoulou and published by . This book was released on 2008 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: We presented several clustering methods for dimensionality reduction to improve text classification. Experiments show that one-way clustering is more effective than feature selection, especially at lower number of features. Also, when dimensionality is reduced by as much as two orders of magnitude the resulting classification accuracy is similar to a fullfeature classifier. In some cases of small training sets and noisy features, feature clustering can actually increase classification accuracy. In the case of IB, various heuristics can be applied in order to obtain finer clusters, greedy agglomerative hard clustering (Slonim & Tishby, 1999), or a sequential K-means like algorithm (Slonim et al., 2002). Co-clustering methods are superior to one-way clustering methods as shown through corresponding experiments (Takamura, 2003). Benefits of using one-way clustering and co-clustering as a feature compression and/or extraction method include: useful semantic feature clusters, higher classification accuracy (via noise reduction), and smaller classification models. The second two reasons are shared with feature selection, and thus clustering can be seen as an alternative or a complement to feature selection, although it does not actually remove any features. Clustering is better at reducing the number of redundant features, whereas feature selection is better at removing detrimental, noisy features. The reduced dimensionality allows the use of more complex algorithms, and reduces computational burden. However, it is necessary to experimentally evaluate the trade-off between soft and hard clustering. While soft clustering increases the classification model size, it is not clear how it affects classification accuracy. Other directions for exploration include feature weighting and combination of feature selection and clustering strategies. There are four cases of semi-supervised classification using clustering considered in the area. In the first case, in the absence of a labelled set, clustering is used to create one by selecting unlabelled data from a pool of available unlabelled data. In the second case, it is used to augment an existing labelled set with new documents from the unlabelled data. In the third case, the dataset is augmented with new features derived from clustering labelled and unlabelled data. In the last case, clustering is used under a co-training framework. The algorithms presented demonstrate effective use of unlabelled data and significant improvements in classification performance especially when the size of the labelled set is small. In most experiments, the unlabelled data come from the same information source as the training and testing sets. Since the feature distribution of the unlabelled data is crucial to the success of the method, an area of future research is the effect of the source and nature of information in the unlabelled dataset and clustering. Lastly, clustering reduces the training time of the SVM i) by modifying the SVM algorithm so that it can be applied to large data sets, and ii) by finding and using for training only the most qualified training examples of a large data set and disqualifying unimportant ones. A clustering algorithm and a classifier cooperate and act interchangeably and complementary. In the first case, many algorithms have been proposed (sequential minimal optimisation, projected conjugate gradient, neural networks amongst others) in order to simplify the training process of SVM, usually by breaking down the problem into smaller sub-problems easier to solve. In the second case, the training set is clustered in order to select the most representative examples to train a classifier instead of using the whole training set. The clustering results are used differently by the various approaches, i.e. the selection of the representative training examples follows different methods. Some of the proposed algorithms manage to decrease the number of training examples without compromising the.
Book Synopsis Image and Video Retrieval by : Wee-Kheng Leow
Download or read book Image and Video Retrieval written by Wee-Kheng Leow and published by Springer. This book was released on 2007-05-22 with total page 686 pages. Available in PDF, EPUB and Kindle. Book excerpt: It was our great pleasure to host the 4th International Conference on Image and Video Retrieval (CIVR) at the National University of Singapore on 20–22 July 2005. CIVR aims to provide an international forum for the discussion of research challenges and exchange of ideas among researchers and practitioners in image/video retrieval technologies. It addresses innovative research in the broad ?eld of image and video retrieval. A unique feature of this conference is the high level of participation by researchers from both academia and industry. Another unique feature of CIVR this year was in its format – it o?ered both the traditional oral presentation sessions, as well as the short presentation cum poster sessions. The latter provided an informal alternative forum for animated discussions and exchanges of ideas among the participants. We are pleased to note that interest in CIVR has grown over the years. The number of submissions has steadily increased from 82 in 2002, to 119 in 2003, and 125 in 2004. This year, we received 128 submissions from the international communities:with81(63.3%)fromAsiaandAustralia,25(19.5%)fromEurope, and 22 (17.2%) from North America. After a rigorous review process, 20 papers were accepted for oral presentations, and 42 papers were accepted for poster presentations. In addition to the accepted submitted papers, the program also included 4 invited papers, 1 keynote industrial paper, and 4 invited industrial papers. Altogether, we o?ered a diverse and interesting program, addressing the current interests and future trends in this area.
Book Synopsis Artificial Intelligence Methods for Optimization of the Software Testing Process by : Sahar Tahvili
Download or read book Artificial Intelligence Methods for Optimization of the Software Testing Process written by Sahar Tahvili and published by Academic Press. This book was released on 2022-07-21 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence Methods for Optimization of the Software Testing Process: With Practical Examples and Exercises presents different AI-based solutions for overcoming the uncertainty found in many initial testing problems. The concept of intelligent decision making is presented as a multi-criteria, multi-objective undertaking. The book provides guidelines on how to manage diverse types of uncertainty with intelligent decision-making that can help subject matter experts in many industries improve various processes in a more efficient way. As the number of required test cases for testing a product can be large (in industry more than 10,000 test cases are usually created). Executing all these test cases without any particular order can impact the results of the test execution, hence this book fills the need for a comprehensive resource on the topics on the how's, what's and whys. To learn more about Elsevier’s Series, Uncertainty, Computational Techniques and Decision Intelligence, please visit this link: https://www.elsevier.com/books-and-journals/book-series/uncertainty-computational-techniques-and-decision-intelligence Presents one of the first empirical studies in the field, contrasting theoretical assumptions on innovations in a real industrial environment with a large set of use cases from developed and developing testing processes at various large industries Explores specific comparative methodologies, focusing on developed and developing AI-based solutions Serves as a guideline for conducting industrial research in the artificial intelligence and software testing domain Explains all proposed solutions through real industrial case studies
Book Synopsis Encyclopedia of Business Analytics and Optimization by : Wang, John
Download or read book Encyclopedia of Business Analytics and Optimization written by Wang, John and published by IGI Global. This book was released on 2014-02-28 with total page 2862 pages. Available in PDF, EPUB and Kindle. Book excerpt: As the age of Big Data emerges, it becomes necessary to take the five dimensions of Big Data- volume, variety, velocity, volatility, and veracity- and focus these dimensions towards one critical emphasis - value. The Encyclopedia of Business Analytics and Optimization confronts the challenges of information retrieval in the age of Big Data by exploring recent advances in the areas of knowledge management, data visualization, interdisciplinary communication, and others. Through its critical approach and practical application, this book will be a must-have reference for any professional, leader, analyst, or manager interested in making the most of the knowledge resources at their disposal.
Book Synopsis Recent Trends in Image Processing and Pattern Recognition by : K. C. Santosh
Download or read book Recent Trends in Image Processing and Pattern Recognition written by K. C. Santosh and published by Springer. This book was released on 2019-07-16 with total page 751 pages. Available in PDF, EPUB and Kindle. Book excerpt: This three-book set constitutes the refereed proceedings of the Second International Conference on Recent Trends in Image Processing and Pattern Recognition (RTIP2R) 2018, held in Solapur, India, in December 2018. The 173 revised full papers presented were carefully reviewed and selected from 374 submissions. The papers are organized in topical sections in the tree volumes. Part I: computer vision and pattern recognition; machine learning and applications; and image processing. Part II: healthcare and medical imaging; biometrics and applications. Part III: document image analysis; image analysis in agriculture; and data mining, information retrieval and applications.
