Machine Learning and Knowledge Discovery in Databases

Get to know the ‘why’ and ‘how’ of machine learning and big data in quantitative investmentBig Data and Machine Learning in Quantitative Investment is not just about demonstrat

Source Separation and Machine Learning presents the fundamentals in adaptive learning algorithms for Blind Source Separation (BSS) and emphasizes the importance of machine learning perspectives. It il

This new edition continues to illustrate the power of biological data in knowledge discovery. It describes biological data types and representations with examples for creating a workflow in bioinforma

"This book explores what drives profitability and more successful customer relationship management. It looks the behavior of any classifier used to classify sentiments in product reviews. It also deve

"This book analyzes various aspects of the knowledge society. It anchors government policies and examines the characteristics of the emerging knowledge intensive economy and social environment. It inv

Written by leading researchers, this complete introduction brings together all the theory and tools needed for building robust machine learning in adversarial environments. Discover how machine learning systems can adapt when an adversary actively poisons data to manipulate statistical inference, learn the latest practical techniques for investigating system security and performing robust data analysis, and gain insight into new approaches for designing effective countermeasures against the latest wave of cyber-attacks. Privacy-preserving mechanisms and the near-optimal evasion of classifiers are discussed in detail, and in-depth case studies on email spam and network security highlight successful attacks on traditional machine learning algorithms. Providing a thorough overview of the current state of the art in the field, and possible future directions, this groundbreaking work is essential reading for researchers, practitioners and students in computer security and machine learning,

Adopting Bayesian network as the framework of knowledge representation and inferences, the Chinese authors explore new approaches to uncertain knowledge discovery and fusion by incorporating the m

Recently, a new field of computer science was derived, including methods and techniques of problem solving that cannot be easily described by traditional algorithms. This field, called "cognitive comp

Key FeaturesSecond edition of the bestselling book on Machine LearningA practical approach to key frameworks in data science, machine learning, and deep learningUse the most powerful Python libraries

This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact a

Machine Learning and Medical Imaging presents state-of- the-art machine learning methods in medical image analysis. It first summarizes cutting-edge machine learning algorithms in medical imaging, inc

This book presents the features and advantages offered by complex networks in the machine learning domain. In the first part, an overview on complex networks and network-based machine learning is pres

?This book provides a complete overview of the role of machine learning in radiation oncology and medical physics, covering basic theory, methods, and a variety of applications in medical physics and

With the advent of electronic databases, information technologies, and the Internet, organizations now more than ever have easy access to all the knowledge they need to conduct their affairs. Identify

Offering a fundamental basis in kernel-based learning theory, this book covers both statistical and algebraic principles. It provides over 30 major theorems for kernel-based supervised and unsupervised learning models. The first of the theorems establishes a condition, arguably necessary and sufficient, for the kernelization of learning models. In addition, several other theorems are devoted to proving mathematical equivalence between seemingly unrelated models. With over 25 closed-form and iterative algorithms, the book provides a step-by-step guide to algorithmic procedures and analysing which factors to consider in tackling a given problem, enabling readers to improve specifically designed learning algorithms, build models for new applications and develop efficient techniques suitable for green machine learning technologies. Numerous real-world examples and over 200 problems, several of which are Matlab-based simulation exercises, make this an essential resource for graduate student

The amount of data in medical databases doubles every 20 months, and physicians are at a loss to analyze them. Also, traditional methods of data analysis have difficulty to identify outliers and patte

Offering sequenced guidance for non-specialists on how to reap the benefits of machine learning in medicine and healthcare, this text harnesses the power of cutting-edge computing to maximize the acce

The book adopts a tutorial-based approach to introduce the user to Scikit-learn. If you are a programmer who wants to explore machine learning and data-based methods to build intelligent applications

Currently there are major challenges in data mining applications in the geosciences. This is due primarily to the fact that there is a wealth of available mining data amid an absence of the knowledge

Providing a unique and integrated compendium of both current and emerging machine-learning paradigms in the vital field of health informatics, this work by leading experts reflects the diversity and c

"This book investigates the latest developments in nature-influenced technologies, offering an in-depth analysis of such advances as cryptographic solutions based on cell division, the creation and ma

Machine learning is concerned with the analysis of large data and multiple variables. However, it is also often more sensitive than traditional statistical methods to analyze small data. The first vol

Machine learning is a novel discipline concerned with the analysis of large and multiple variables data. It involves computationally intensive methods, like factor analysis, cluster analysis, and disc

This book presents some of the most recent research results in the area of machine learning and robot perception. The chapters represent new ways of solving real-world problems. The book covers topics

This volume features key contributions from the International Conference on Pattern Recognition Applications and Methods, (ICPRAM 2012,) held in Vilamoura, Algarve, Portugal from February 6th-8th, 201

This book presents fundamental topics and algorithms that form the core of machine learning (ML) research, as well as emerging paradigms in intelligent system design. The multidisciplinary nature of

Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the methods have their choice of rewarding jobs. This hands-on text opens these opportunities to computer science students with modest mathematical backgrounds. It is designed for final-year undergraduates and master's students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models. Students learn more than a menu of techniques, they develop analytical and problem-solving skills that equip them for the real world. Numerous examples and exercises, both computer based and theoretical, are included in every chapter. Resources for students and instructors,

This book contains the extended papers presented at the 3rd Workshop on Supervised and Unsupervised Ensemble Methods and their Applications (SUEMA) that was held in conjunction with the European Confe

This book constitutes the refereed proceedings of the 13th International Conference on Data Warehousing and Knowledge Discovery, DaWak 2011 held in Toulouse, France in August/September 2011. The 37 re

Phase transitions typically occur in combinatorial computational problems and have important consequences, especially with the current spread of statistical relational learning as well as sequence learning methodologies. In Phase Transitions in Machine Learning the authors begin by describing in detail this phenomenon, and the extensive experimental investigation that supports its presence. They then turn their attention to the possible implications and explore appropriate methods for tackling them. Weaving together fundamental aspects of computer science, statistical physics and machine learning, the book provides sufficient mathematics and physics background to make the subject intelligible to researchers in AI and other computer science communities. Open research issues are also discussed, suggesting promising directions for future research.

With the rapid advancement of information discovery techniques, machine learning and data mining continue to play a significant role in cybersecurity. Although several conferences, workshops, and jour

The objective of Document Analysis and Recognition (DAR) is to recognize the text and graphical components of a document and to extract information. This book is a collection of research papers and st

This book constitutes the refereed proceedings of the 12th International Conference on Data Warehousing and Knowledge Discovery, DaWak 2010 held in Bilbao, Spain in August/September 2010.The 26 revise

This book contains thoroughly refereed extended papers from the Second International Workshop on Knowledge Discovery from Sensor Data, Sensor-KDD 2008, held in Las Vegas, NV, USA, in August 2008. The

A large international conference on Advances in Machine Learning and Data Analysis was held in UC Berkeley, California, USA, October 22-24, 2008, under the auspices of the World Congress on Engineerin