Download or read book Entropy Guided Transformation Learning Algorithms and Applications written by Cícero Nogueira dos Santos and published by Springer Science & Business Media. This book was released on 2012-03-14 with total page 81 pages. Available in PDF, EPUB and Kindle. Book excerpt: Entropy Guided Transformation Learning: Algorithms and Applications (ETL) presents a machine learning algorithm for classification tasks. ETL generalizes Transformation Based Learning (TBL) by solving the TBL bottleneck: the construction of good template sets. ETL automatically generates templates using Decision Tree decomposition. The authors describe ETL Committee, an ensemble method that uses ETL as the base learner. Experimental results show that ETL Committee improves the effectiveness of ETL classifiers. The application of ETL is presented to four Natural Language Processing (NLP) tasks: part-of-speech tagging, phrase chunking, named entity recognition and semantic role labeling. Extensive experimental results demonstrate that ETL is an effective way to learn accurate transformation rules, and shows better results than TBL with handcrafted templates for the four tasks. By avoiding the use of handcrafted templates, ETL enables the use of transformation rules to a greater range of tasks. Suitable for both advanced undergraduate and graduate courses, Entropy Guided Transformation Learning: Algorithms and Applications provides a comprehensive introduction to ETL and its NLP applications.

Download or read book Foundations of Computational Intelligence written by Aboul-Ella Hassanien and published by Springer. This book was released on 2009-05-02 with total page 401 pages. Available in PDF, EPUB and Kindle. Book excerpt: Foundations of Computational Intelligence Volume 1: Learning and Approximation: Theoretical Foundations and Applications Learning methods and approximation algorithms are fundamental tools that deal with computationally hard problems and problems in which the input is gradually disclosed over time. Both kinds of problems have a large number of applications arising from a variety of fields, such as algorithmic game theory, approximation classes, coloring and partitioning, competitive analysis, computational finance, cuts and connectivity, inapproximability results, mechanism design, network design, packing and covering, paradigms for design and analysis of approxi- tion and online algorithms, randomization techniques, real-world applications, scheduling problems and so on. The past years have witnessed a large number of interesting applications using various techniques of Computational Intelligence such as rough sets, connectionist learning; fuzzy logic; evolutionary computing; artificial immune systems; swarm intelligence; reinforcement learning, intelligent multimedia processing etc. . In spite of numerous successful applications of C- putational Intelligence in business and industry, it is sometimes difficult to explain the performance of these techniques and algorithms from a theoretical perspective. Therefore, we encouraged authors to present original ideas dealing with the inc- poration of different mechanisms of Computational Intelligent dealing with Lea- ing and Approximation algorithms and underlying processes. This edited volume comprises 15 chapters, including an overview chapter, which provides an up-to-date and state-of-the art research on the application of Computational Intelligence for learning and approximation.

Download or read book Computational Processing of the Portuguese Language written by Helena Caseli and published by Springer. This book was released on 2012-03-09 with total page 443 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed proceedings of the 8th International Workshop on Computational Processing of the Portuguese Language, PROPOR 2012, held in Coimbra, Portugal in April 2012. The 24 revised full papers and 23 revised short papers presented were carefully reviewed and selected from 86 submissions. These papers cover the areas related to phonology, morphology and POS-Tagging, acquisition, language resources, linguistic description, syntax and parsing, semantics, opinion analysis, natural language processing applications, speech production and phonetics, speech resources, speech processing and applications.

Download or read book Computational Linguistics and Intelligent Text Processing written by Alexander Gelbukh and published by Springer. This book was released on 2010-03-17 with total page 778 pages. Available in PDF, EPUB and Kindle. Book excerpt: th CICLing 2010 was the 11 Annual Conference on Intelligent Text Processing and Computational Linguistics. The CICLing conferences provide a wide-scope forum for discussion of the art and craft of natural language processing research as well as the best practices in its applications. This volume contains three invited papers and the regular papers accepted for oral presentation at the conference. The papers accepted for poster pres- tation were published in a special issue of another journal (see information on thewebsite).Since 2001,theproceedingsofCICLingconferenceshavebeen p- lished in Springer’s Lecture Notes in Computer Science series, as volumes 2004, 2276, 2588, 2945, 3406, 3878, 4394, 4919, and 5449. The volume is structured into 12 sections: – Lexical Resources – Syntax and Parsing – Word Sense Disambiguation and Named Entity Recognition – Semantics and Dialog – Humor and Emotions – Machine Translation and Multilingualism – Information Extraction – Information Retrieval – Text Categorization and Classi?cation – Plagiarism Detection – Text Summarization – Speech Generation The 2010 event received a record high number of submissions in the - year history of the CICLing series. A total of 271 papers by 565 authors from 47 countriesweresubmittedforevaluationbytheInternationalProgramCommittee (see Tables 1 and 2). This volume contains revised versions of 61 papers, by 152 authors, selected for oral presentation; the acceptance rate was 23%.

Download or read book Understanding Machine Learning written by Shai Shalev-Shwartz and published by Cambridge University Press. This book was released on 2014-05-19 with total page 415 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.

Download or read book Nature Inspired Cooperative Strategies for Optimization NICSO 2013 written by German Terrazas and published by Springer. This book was released on 2013-08-15 with total page 357 pages. Available in PDF, EPUB and Kindle. Book excerpt: Biological and other natural processes have always been a source of inspiration for computer science and information technology. Many emerging problem solving techniques integrate advanced evolution and cooperation strategies, encompassing a range of spatio-temporal scales for visionary conceptualization of evolutionary computation. This book is a collection of research works presented in the VI International Workshop on Nature Inspired Cooperative Strategies for Optimization (NICSO) held in Canterbury, UK. Previous editions of NICSO were held in Granada, Spain (2006 & 2010), Acireale, Italy (2007), Tenerife, Spain (2008), and Cluj-Napoca, Romania (2011). NICSO 2013 and this book provides a place where state-of-the-art research, latest ideas and emerging areas of nature inspired cooperative strategies for problem solving are vigorously discussed and exchanged among the scientific community. The breadth and variety of articles in this book report on nature inspired methods and applications such as Swarm Intelligence, Hyper-heuristics, Evolutionary Algorithms, Cellular Automata, Artificial Bee Colony, Dynamic Optimization, Support Vector Machines, Multi-Agent Systems, Ant Clustering, Evolutionary Design Optimisation, Game Theory and other several Cooperation Models.

Download or read book Smart Information Systems written by Frank Hopfgartner and published by Springer. This book was released on 2015-01-14 with total page 378 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text presents an overview of smart information systems for both the private and public sector, highlighting the research questions that can be studied by applying computational intelligence. The book demonstrates how to transform raw data into effective smart information services, covering the challenges and potential of this approach. Each chapter describes the algorithms, tools, measures and evaluations used to answer important questions. This is then further illustrated by a diverse selection of case studies reflecting genuine problems faced by SMEs, multinational manufacturers, service companies, and the public sector. Features: provides a state-of-the-art introduction to the field, integrating contributions from both academia and industry; reviews novel information aggregation services; discusses personalization and recommendation systems; examines sensor-based knowledge acquisition services, describing how the analysis of sensor data can be used to provide a clear picture of our world.

Download or read book Adaptive and Natural Computing Algorithms written by Marco Tomassini and published by Springer. This book was released on 2013-04-12 with total page 518 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book constitutes the refereed proceedings of the 11th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2013, held in Lausanne, Switzerland, in April 2013. The 51 revised full papers presented were carefully reviewed and selected from a total of 91 submissions. The papers are organized in topical sections on neural networks, evolutionary computation, soft computing, bioinformatics and computational biology, advanced computing, and applications.

Download or read book Entropy Measures for Data Analysis written by Karsten Keller and published by MDPI. This book was released on 2019-12-19 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: Entropies and entropy-like quantities play an increasing role in modern non-linear data analysis. Fields that benefit from this application range from biosignal analysis to econophysics and engineering. This issue is a collection of papers touching on different aspects of entropy measures in data analysis, as well as theoretical and computational analyses. The relevant topics include the difficulty to achieve adequate application of entropy measures and the acceptable parameter choices for those entropy measures, entropy-based coupling, and similarity analysis, along with the utilization of entropy measures as features in automatic learning and classification. Various real data applications are given.

Download or read book High Entropy Materials Theory Experiments and Applications written by Jamieson Brechtl and published by Springer Nature. This book was released on 2022-01-03 with total page 776 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses fundamental studies involving the history, modelling, simulation, experimental work, and applications on high-entropy materials. Topics include data-driven and machine-learning approaches, additive-manufacturing techniques, computational and analytical methods, such as density functional theory and multifractal analysis, mechanical behavior, high-throughput methods, and irradiation effects. The types of high-entropy materials consist of alloys, oxides, and ceramics. The book then concludes with a discussion on potential future applications of these novel materials.

Download or read book Machine Learning Refined written by Jeremy Watt and published by Cambridge University Press. This book was released on 2020-01-09 with total page 597 pages. Available in PDF, EPUB and Kindle. Book excerpt: An intuitive approach to machine learning covering key concepts, real-world applications, and practical Python coding exercises.

Download or read book Unmanned Aerial Vehicle Applications over Cellular Networks for 5G and Beyond written by Hongliang Zhang and published by Springer Nature. This book was released on 2019-12-13 with total page 231 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses how to plan the time-variant placements of the UAVs served as base station (BS)/relay, which is very challenging due to the complicated 3D propagation environments, as well as many other practical constraints such as power and flying speed. Spectrum sharing with existing cellular networks is also investigated in this book. The emerging unmanned aerial vehicles (UAVs) have been playing an increasing role in the military, public, and civil applications. To seamlessly integrate UAVs into future cellular networks, this book will cover two main scenarios of UAV applications as follows. The first type of applications can be referred to as UAV Assisted Cellular Communications. Second type of application is to exploit UAVs for sensing purposes, such as smart agriculture, security monitoring, and traffic surveillance. Due to the limited computation capability of UAVs, the real-time sensory data needs to be transmitted to the BS for real-time data processing. The cellular networks are necessarily committed to support the data transmission for UAVs, which the authors refer to as Cellular assisted UAV Sensing. To support real-time sensing streaming, the authors design joint sensing and communication protocols, develop novel beamforming and estimation algorithms, and study efficient distributed resource optimization methods. This book targets signal processing engineers, computer and information scientists, applied mathematicians and statisticians, as well as systems engineers to carve out the role that analytical and experimental engineering has to play in UAV research and development. Undergraduate students, industry managers, government research agency workers and general readers interested in the fields of communications and networks will also want to read this book.

Download or read book Python Advanced Guide to Artificial Intelligence written by Giuseppe Bonaccorso and published by Packt Publishing Ltd. This book was released on 2018-12-21 with total page 748 pages. Available in PDF, EPUB and Kindle. Book excerpt: Demystify the complexity of machine learning techniques and create evolving, clever solutions to solve your problems Key FeaturesMaster supervised, unsupervised, and semi-supervised ML algorithms and their implementation Build deep learning models for object detection, image classification, similarity learning, and moreBuild, deploy, and scale end-to-end deep neural network models in a production environmentBook Description This Learning Path is your complete guide to quickly getting to grips with popular machine learning algorithms. You'll be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this Learning Path will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries. You'll bring the use of TensorFlow and Keras to build deep learning models, using concepts such as transfer learning, generative adversarial networks, and deep reinforcement learning. Next, you'll learn the advanced features of TensorFlow1.x, such as distributed TensorFlow with TF clusters, deploy production models with TensorFlow Serving. You'll implement different techniques related to object classification, object detection, image segmentation, and more. By the end of this Learning Path, you'll have obtained in-depth knowledge of TensorFlow, making you the go-to person for solving artificial intelligence problems This Learning Path includes content from the following Packt products: Mastering Machine Learning Algorithms by Giuseppe BonaccorsoMastering TensorFlow 1.x by Armando FandangoDeep Learning for Computer Vision by Rajalingappaa ShanmugamaniWhat you will learnExplore how an ML model can be trained, optimized, and evaluatedWork with Autoencoders and Generative Adversarial NetworksExplore the most important Reinforcement Learning techniquesBuild end-to-end deep learning (CNN, RNN, and Autoencoders) modelsWho this book is for This Learning Path is for data scientists, machine learning engineers, artificial intelligence engineers who want to delve into complex machine learning algorithms, calibrate models, and improve the predictions of the trained model. You will encounter the advanced intricacies and complex use cases of deep learning and AI. A basic knowledge of programming in Python and some understanding of machine learning concepts are required to get the best out of this Learning Path.

Download or read book Robot Systems for Rail Transit Applications written by Hui Liu and published by Elsevier. This book was released on 2020-06-27 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robot Systems for Rail Transit Applications presents the latest advances in robotics and artificial intelligence for railway systems, giving foundational principles and running through special problems in robot systems for rail transit. State-of-the art research in robotics and railway systems is presented alongside a series of real-world examples. Eight chapters give definitions and characteristics of rail transit robot systems, describe assembly and collaborative robots in manufacturing, introduce automated guided vehicles and autonomous rail rapid transit, demonstrate inspection robots, cover trench robots, and explain unmanned aerial vehicles. This book offers an integrated and highly-practical way to approach robotics and artificial intelligence in rail-transit. - Introduces robot and artificial intelligence (AI) systems for rail transit applications - Presents research alongside step-by-step coverage of real-world cases - Gives the theoretical foundations underlying practical application - Offers solutions for high-speed railways from the latest work in robotics - Shows how robotics and AI systems afford new and efficient methods in rail transit

Download or read book Machine Learning Algorithms written by Giuseppe Bonaccorso and published by Packt Publishing Ltd. This book was released on 2017-07-24 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive guide About This Book Get started in the field of Machine Learning with the help of this solid, concept-rich, yet highly practical guide. Your one-stop solution for everything that matters in mastering the whats and whys of Machine Learning algorithms and their implementation. Get a solid foundation for your entry into Machine Learning by strengthening your roots (algorithms) with this comprehensive guide. Who This Book Is For This book is for IT professionals who want to enter the field of data science and are very new to Machine Learning. Familiarity with languages such as R and Python will be invaluable here. What You Will Learn Acquaint yourself with important elements of Machine Learning Understand the feature selection and feature engineering process Assess performance and error trade-offs for Linear Regression Build a data model and understand how it works by using different types of algorithm Learn to tune the parameters of Support Vector machines Implement clusters to a dataset Explore the concept of Natural Processing Language and Recommendation Systems Create a ML architecture from scratch. In Detail As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of Big Data and Data Science. The main challenge is how to transform data into actionable knowledge. In this book you will learn all the important Machine Learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. A few famous algorithms that are covered in this book are Linear regression, Logistic Regression, SVM, Naive Bayes, K-Means, Random Forest, TensorFlow, and Feature engineering. In this book you will also learn how these algorithms work and their practical implementation to resolve your problems. This book will also introduce you to the Natural Processing Language and Recommendation systems, which help you run multiple algorithms simultaneously. On completion of the book you will have mastered selecting Machine Learning algorithms for clustering, classification, or regression based on for your problem. Style and approach An easy-to-follow, step-by-step guide that will help you get to grips with real -world applications of Algorithms for Machine Learning.

Download or read book Mathematics for Machine Learning written by Marc Peter Deisenroth and published by Cambridge University Press. This book was released on 2020-04-23 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.

Download or read book Computer Vision written by E. R. Davies and published by Academic Press. This book was released on 2017-11-15 with total page 902 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computer Vision: Principles, Algorithms, Applications, Learning (previously entitled Computer and Machine Vision) clearly and systematically presents the basic methodology of computer vision, covering the essential elements of the theory while emphasizing algorithmic and practical design constraints. This fully revised fifth edition has brought in more of the concepts and applications of computer vision, making it a very comprehensive and up-to-date text suitable for undergraduate and graduate students, researchers and R&D engineers working in this vibrant subject. See an interview with the author explaining his approach to teaching and learning computer vision - http://scitechconnect.elsevier.com/computer-vision/ - Three new chapters on Machine Learning emphasise the way the subject has been developing; Two chapters cover Basic Classification Concepts and Probabilistic Models; and the The third covers the principles of Deep Learning Networks and shows their impact on computer vision, reflected in a new chapter Face Detection and Recognition. - A new chapter on Object Segmentation and Shape Models reflects the methodology of machine learning and gives practical demonstrations of its application. - In-depth discussions have been included on geometric transformations, the EM algorithm, boosting, semantic segmentation, face frontalisation, RNNs and other key topics. - Examples and applications—including the location of biscuits, foreign bodies, faces, eyes, road lanes, surveillance, vehicles and pedestrians—give the 'ins and outs' of developing real-world vision systems, showing the realities of practical implementation. - Necessary mathematics and essential theory are made approachable by careful explanations and well-illustrated examples. - The 'recent developments' sections included in each chapter aim to bring students and practitioners up to date with this fast-moving subject. - Tailored programming examples—code, methods, illustrations, tasks, hints and solutions (mainly involving MATLAB and C++)