Natural Language Annotation for Machine Learning

Machine learning, one of the top emerging sciences, has an extremely broad range of applications. However, many books on the subject provide only a theoretical approach, making it difficult for a newc

This book covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. The entire text, including all the figures and n

This book presents machine learning models and algorithms to address big data classification problems. Existing machine learning techniques like the decision tree (a hierarchical approach), random for

The fourth edition of Language and Learning continues to provide an accessible, comprehensive explanation of how language can be understood. Written specifically for Australian teacher-education stude

Machine learning allows computers to learn and discern patterns without actually being programmed. When Statistical techniques and machine learning are combined together they are a powerful tool for a

Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book Leverage Python's most powerful open-source libraries for deep learning, data wr

Building on their best-selling book Flip Your Classroom: Reach Every Student in Every Class Every Day, flipped education innovators Jonathan Bergmann and Aaron Sams return with a book series that supp

Written as a tutorial to explore and understand the power of R for machine learning. This practical guide that covers all of the need to know topics in a very systematic way. For each machine learning

Wan and Mak describe machine-learning approaches to determining automatically the location of a protein within a cell in order to facilitate drug discovery and drug design. The approaches exploit the

Learn a simpler and more effective way to analyze data and predict outcomes with PythonMachine Learning in Python shows you how to successfully analyze data using only two core machine learning algori

Dig deep into the data with a hands-on guide to machine learningMachine Learning: Hands-On for Developers and Technical Professionals provides hands-on instruction and fully-coded working examples for

Elements of Success is a new grammar course which helps students learn the real-world grammar they need to read, communicate, and write effectively. Grammar is clearly presented with highly visual cha

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

In the past few decades the use of increasingly large text corpora has grown rapidly in language and linguistics research. This was enabled by remarkable strides in natural language processing (NLP) t

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

With the advent and increasing popularity of Computer Supported Collaborative Learning (CSCL) and e-learning technologies, the need of automatic assessment and of teacher/tutor support for the two tig

Reinforcement learning (RL) is a framework for decision making in unknown environments based on a large amount of data. Several practical RL applications for business intelligence, plant control, and

Written as a tutorial to explore and understand the power of R for machine learning. This practical guide that covers all of the need to know topics in a very systematic way. For each machine learning

This thesis discusses the privacy issues in speech-based applications such as biometric authentication, surveillance, and external speech processing services. Author Manas A. Pathak presents solutions

Today's Web-enabled deluge of electronic data calls for automated methods of dataanalysis. Machine learning provides these, developing methods that can automatically detect patternsin data and then us

Reinforcement and Systemic Machine Learning for Decision MakingThere are always difficulties in making machines that learn from experience. Complete information is not always available—or it beco

Explore the multidisciplinary nature of complex networks through machine learning techniquesStatistical and Machine Learning Approaches for Network Analysis provides an accessible framework for struct

SummaryMachine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the fle

Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of stat

This book concerns non-linguistic knowledge required to perform computational natural language understanding (NLU). The main objective of the book is to show that inference-based NLU has the potential

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 presents an integrated collection of representative approaches for scaling up machine learning and data mining methods on parallel and distributed computing platforms. Demand for parallelizing learning algorithms is highly task-specific: in some settings it is driven by the enormous dataset sizes, in others by model complexity or by real-time performance requirements. Making task-appropriate algorithm and platform choices for large-scale machine learning requires understanding the benefits, trade-offs and constraints of the available options. Solutions presented in the book cover a range of parallelization platforms from FPGAs and GPUs to multi-core systems and commodity clusters, concurrent programming frameworks including CUDA, MPI, MapReduce and DryadLINQ, and learning settings (supervised, unsupervised, semi-supervised and online learning). Extensive coverage of parallelization of boosted trees, SVMs, spectral clustering, belief propagation and other popular learning algo

"Machine Learning and Data Mining for Computer Security" provides an overview of the current state of research in machine learning and data mining as it applies to problems in computer security. This

In machine learning applications, practitioners must take into account the cost associated with the algorithm. These costs include: Cost of acquiring training dataCost of data annotation/labeling and

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.

Decision trees and decision rule systems are widely used in different applicationsas algorithms for problem solving, as predictors, and as a way forknowledge representation. Reducts play key role in t

Machine learning forensics can be used to recognize patterns of criminal activity, detect network intrusions, and discover evidence. Mena, an artificial intelligence specialist, compiles deductive and

Data fusion problems arise frequently in many different fields. This book provides a specific introduction to data fusion problems using support vector machines. In the first part, this book begins w

This book covers a wide range of subjects in applying machine learning approaches for bioinformatics projects. The book succeeds on two key unique features. First, it introduces the most widely used m

This work gathers recent international research on vision-based systems for analyzing and interpreting motion from video footage. Chapter topics include graphical models for representation and recogni

This book demonstrates the possibility of transforming machine learning algorithms into integrated commonsense reasoning processes in which inductive and deductive inferences correlate and support one

This book is a thorough introduction to the most important topics in data mining and machine learning. It begins with a detailed review of classical function estimation and proceeds with chapters on

Drawing on recent socio-cultural approaches to research on language learning and an extensive corpus of classroom video recording made over four years, the book documents language learning as an epiph