Machine Learning Engineering in Action lays out an approach to building deployable, maintainable production machine learning systems. You will adopt software development standards that deliver better code management, and make it easier to test, scale, and even reuse your machine learning code! You will learn how to plan and scope your project, manage cross-team logistics that avoid fatal communication failures, and design your code's architecture for improved resilience. You will even discover when not to use machine learning-and the alternative approaches that might be cheaper and more effective. When you're done working through this toolbox guide, you will be able to reliably deliver cost-effective solutions for organizations big and small alike. Following established processes and methodology maximizes the likelihood that your machine learning projects will survive and succeed for the long haul. By adopting standard, reproducible practices, your projects will be maintainable over time and easy for new team members to understand and adapt.

This volume constitutes the thoroughly refereed post-conference proceedings of the International Workshop on Adaptive and Learning Agents, ALA 2011, held at the 10th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2011, in Taipei, Taiwan, in May 2011. The 7 revised full papers presented together with 1 invited talk were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on single and multi-agent reinforcement learning, supervised multiagent learning, adaptation and learning in dynamic environments, learning trust and reputation, minority games and agent coordination.

The research in content-based indexing and retrieval of visual information such as images and video has become one of the most populated directions in the vast area of information technologies. Social networks such as YouTube, Facebook, FileMobile, and DailyMotion host and supply facilities for accessing a tremendous amount of professional and user generated data. The areas of societal activity, such as, video protection and security, also generate thousands and thousands of terabytes of visual content. This book presents the most recent results and important trends in visual information indexing and retrieval. It is intended for young researchers, as well as, professionals looking for an algorithmic solution to a problem.

With the rapid rise in the ubiquity and sophistication of Internet technology and the accompanying growth in the number of network attacks, network intrusion detection has become increasingly important. Anomaly-based network intrusion detection refers to finding exceptional or nonconforming patterns in network traffic data compared to normal behavior. Finding these anomalies has extensive applications in areas such as cyber security, credit card and insurance fraud detection, and military surveillance for enemy activities. Network Anomaly Detection: A Machine Learning Perspective presents machine learning techniques in depth to help you more effectively detect and counter network intrusion. In this book, you'll learn about: Network anomalies and vulnerabilities at various layers The pros and cons of various machine learning techniques and algorithms A taxonomy of attacks based on their characteristics and behavior Feature selection algorithms How to assess the accuracy, performance, completeness, timeliness, stability, interoperability, reliability, and other dynamic aspects of a network anomaly detection system Practical tools for launching attacks, capturing packet or flow traffic, extracting features, detecting attacks, and evaluating detection performance Important unresolved issues and research challenges that need to be overcome to provide better protection for networks Examining numerous attacks in detail, the authors look at the tools that intruders use and show how to use this knowledge to protect networks. The book also provides material for hands-on development, so that you can code on a testbed to implement detection methods toward the development of your own intrusion detection system. It offers a thorough introduction to the state of the art in network anomaly detection using machine learning approaches and systems.

This book constitutes the refereed proceedings of the 8th International Conference, MLDM 2012, held in Berlin, Germany in July 2012. The 51 revised full papers presented were carefully reviewed and selected from 212 submissions. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multimedia data types such as image mining, text mining, video mining and web mining.

A comprehensive review to the theory, application and research of machine learning for future wireless communications In one single volume, Machine Learning for Future Wireless Communications provides a comprehensive and highly accessible treatment to the theory, applications and current research developments to the technology aspects related to machine learning for wireless communications and networks. The technology development of machine learning for wireless communications has grown explosively and is one of the biggest trends in related academic, research and industry communities. Deep neural networks-based machine learning technology is a promising tool to attack the big challenge in wireless communications and networks imposed by the increasing demands in terms of capacity, coverage, latency, efficiency flexibility, compatibility, quality of experience and silicon convergence. The author - a noted expert on the topic - covers a wide range of topics including system architecture and optimization, physical-layer and cross-layer processing, air interface and protocol design, beamforming and antenna configuration, network coding and slicing, cell acquisition and handover, scheduling and rate adaption, radio access control, smart proactive caching and adaptive resource allocations. Uniquely organized into three categories: Spectrum Intelligence, Transmission Intelligence and Network Intelligence, this important resource: Offers a comprehensive review of the theory, applications and current developments of machine learning for wireless communications and networks Covers a range of topics from architecture and optimization to adaptive resource allocations Reviews state-of-the-art machine learning based solutions for network coverage Includes an overview of the applications of machine learning algorithms in future wireless networks Explores flexible backhaul and front-haul, cross-layer optimization and coding, full-duplex radio, digital front-end (DFE) and radio-frequency (RF) processing Written for professional engineers, researchers, scientists, manufacturers, network operators, software developers and graduate students, Machine Learning for Future Wireless Communications presents in 21 chapters a comprehensive review of the topic authored by an expert in the field.

Professor Richard S. Michalski passed away on September 20, 2007. Once we learned about his untimely death we immediately realized that we would no longer have with us a truly exceptional scholar and researcher who for several decades had been inf- encing the work of numerous scientists all over the world - not only in his area of expertise, notably machine learning, but also in the broadly understood areas of data analysis, data mining, knowledge discovery and many others. In fact, his influence was even much broader due to his creative vision, integrity, scientific excellence and exceptionally wide intellectual horizons which extended to history, political science and arts. Professor Michalski's death was a particularly deep loss to the whole Polish sci- tific community and the Polish Academy of Sciences in particular. After graduation, he began his research career at the Institute of Automatic Control, Polish Academy of Science in Warsaw. In 1970 he left his native country and hold various prestigious positions at top US universities. His research gained impetus and he soon established himself as a world authority in his areas of interest - notably, he was widely cons- ered a father of machine learning.

Although governments worldwide have invested significantly in intelligent sensor network research and applications, few books cover intelligent sensor networks from a machine learning and signal processing perspective. Filling this void, Intelligent Sensor Networks: The Integration of Sensor Networks, Signal Processing and Machine Learning focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on the world-class research of award-winning authors, the book provides a firm grounding in the fundamentals of intelligent sensor networks, including compressive sensing and sampling, distributed signal processing, and intelligent signal learning. Presenting recent research results of world-renowned sensing experts, the book is organized into three parts: Machine Learning-describes the application of machine learning and other AI principles in sensor network intelligence-covering smart sensor/transducer architecture and data representation for intelligent sensors Signal Processing-considers the optimization of sensor network performance based on digital signal processing techniques-including cross-layer integration of routing and application-specific signal processing as well as on-board image processing in wireless multimedia sensor networks for intelligent transportation systems Networking-focuses on network protocol design in order to achieve an intelligent sensor networking-covering energy-efficient opportunistic routing protocols for sensor networking and multi-agent-driven wireless sensor cooperation Maintaining a focus on "intelligent" designs, the book details signal processing principles in sensor networks. It elaborates on critical platforms for intelligent sensor networks and illustrates key applications-including target tracking, object identification, and structural health monitoring. It also includes a paradigm for validating the extent of spatiotemporal associations among data sources to enhance data cleaning in sensor networks, a sensor stream reduction application, and also considers the use of Kalman filters for attack detection in a water system sensor network that consists of water level sensors and velocity sensors.

Human and machine discovery are gradual problem-solving processes of searching large problem spaces for incompletely defined goal objects. Research on problem solving has usually focused on searching an `instance space' (empirical exploration) and a `hypothesis space' (generation of theories). In scientific discovery, searching must often extend to other spaces as well: spaces of possible problems, of new or improved scientific instruments, of new problem representations, of new concepts, and others. This book focuses especially on the processes for finding new problem representations and new concepts, which are relatively new domains for research on discovery. Scientific discovery has usually been studied as an activity of individual investigators, but these individuals are positioned in a larger social structure of science, being linked by the `blackboard' of open publication (as well as by direct collaboration). Even while an investigator is working alone, the process is strongly influenced by knowledge and skills stored in memory as a result of previous social interactions. In this sense, all research on discovery, including the investigations on individual processes discussed in this book, is social psychology, or even sociology.

Machine Learning a branch of Artificial Intelligence is influencing the society, industry and academia at large. The adaptability of Python programming language to Machine Learning has increased its popularity further. Another technology on the horizon is Internet of Things (IoT). The present book tries to address IoT, Python and Machine Learning along with a small introduction to Image Processing. If you are a novice programmer or have just started exploring IoT or Machine Learning with Python, then this book is for you. Features: Raspberry Pi as IoT is described along with the procedure for installation and configuration. A simple introduction to Python Programming Language along with its popular library packages like NumPy, Pandas, SciPy and Matplotlib are dealt in an exhaustive manner along with relevant examples. Machine Learning along with Python Scikit-Learn library is explained to audience with an emphasis on supervised learning and classification. Image processing on IoT is introduced to the audience who love to apply Machine Learning algorithms to Images The book follows hands-on approach and provide a huge collection of Python programs.

This book helps readers understand the mathematics of machine learning, and apply them in different situations. It is divided into two basic parts, the first of which introduces readers to the theory of linear algebra, probability, and data distributions and it's applications to machine learning. It also includes a detailed introduction to the concepts and constraints of machine learning and what is involved in designing a learning algorithm. This part helps readers understand the mathematical and statistical aspects of machine learning. In turn, the second part discusses the algorithms used in supervised and unsupervised learning. It works out each learning algorithm mathematically and encodes it in R to produce customized learning applications. In the process, it touches upon the specifics of each algorithm and the science behind its formulation. The book includes a wealth of worked-out examples along with R codes. It explains the code for each algorithm, and readers can modify the code to suit their own needs. The book will be of interest to all researchers who intend to use R for machine learning, and those who are interested in the practical aspects of implementing learning algorithms for data analysis. Further, it will be particularly useful and informative for anyone who has struggled to relate the concepts of mathematics and statistics to machine learning.

This three-volume set LNAI 6911, LNAI 6912, and LNAI 6913 constitutes the refereed proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2011, held in Athens, Greece, in September 2011. The 121 revised full papers presented together with 10 invited talks and 11 demos in the three volumes, were carefully reviewed and selected from about 600 paper submissions. The papers address all areas related to machine learning and knowledge discovery in databases as well as other innovative application domains such as supervised and unsupervised learning with some innovative contributions in fundamental issues; dimensionality reduction, distance and similarity learning, model learning and matrix/tensor analysis; graph mining, graphical models, hidden markov models, kernel methods, active and ensemble learning, semi-supervised and transductive learning, mining sparse representations, model learning, inductive logic programming, and statistical learning. a significant part of the papers covers novel and timely applications of data mining and machine learning in industrial domains.

Human and Machine Hearing is the first book to comprehensively describe how human hearing works and how to build machines to analyze sounds in the same way that people do. Drawing on over thirty-five years of experience in analyzing hearing and building systems, Richard F. Lyon explains how we can now build machines with close-to-human abilities in speech, music, and other sound-understanding domains. He explains human hearing in terms of engineering concepts, and describes how to incorporate those concepts into machines for a wide range of modern applications. The details of this approach are presented at an accessible level, to bring a diverse range of readers, from neuroscience to engineering, to a common technical understanding. The description of hearing as signal-processing algorithms is supported by corresponding open-source code, for which the book serves as motivating documentation.

The first International Workshop on Machine Learning in Medical Imaging, MLMI 2010, was held at the China National Convention Center, Beijing, China on Sept- ber 20, 2010 in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2010. Machine learning plays an essential role in the medical imaging field, including image segmentation, image registration, computer-aided diagnosis, image fusion, ima- guided therapy, image annotation, and image database retrieval. With advances in me- cal imaging, new imaging modalities, and methodologies such as cone-beam/multi-slice CT, 3D Ultrasound, tomosynthesis, diffusion-weighted MRI, electrical impedance to- graphy, and diffuse optical tomography, new machine-learning algorithms/applications are demanded in the medical imaging field. Single-sample evidence provided by the patient's imaging data is often not sufficient to provide satisfactory performance; the- fore tasks in medical imaging require learning from examples to simulate a physician's prior knowledge of the data. The MLMI 2010 is the first workshop on this topic. The workshop focuses on major trends and challenges in this area, and works to identify new techniques and their use in medical imaging. Our goal is to help advance the scientific research within the broad field of medical imaging and machine learning. The range and level of submission for this year's meeting was of very high quality. Authors were asked to submit full-length papers for review. A total of 38 papers were submitted to the workshop in response to the call for papers.

This two volume set (LNCS 6791 and LNCS 6792) constitutes the refereed proceedings of the 21th International Conference on Artificial Neural Networks, ICANN 2011, held in Espoo, Finland, in June 2011.
The 106 revised full or poster papers presented were carefully reviewed and selected from numerous submissions. ICANN 2011 had two basic tracks: brain-inspired computing and machine learning research, with strong cross-disciplinary interactions and applications.

Informatics and Machine Learning Discover a thorough exploration of how to use computational, algorithmic, statistical, and informatics methods to analyze digital data Informatics and Machine Learning: From Martingales to Metaheuristics delivers an interdisciplinary presentation on how analyze any data captured in digital form. The book describes how readers can conduct analyses of text, general sequential data, experimental observations over time, stock market and econometric histories, or symbolic data, like genomes. It contains large amounts of sample code to demonstrate the concepts contained within and assist with various levels of project work. The book offers a complete presentation of the mathematical underpinnings of a wide variety of forms of data analysis and provides extensive examples of programming implementations. It is based on two decades worth of the distinguished author's teaching and industry experience. A thorough introduction to probabilistic reasoning and bioinformatics, including Python shell scripting to obtain data counts, frequencies, probabilities, and anomalous statistics, or use with Bayes' rule An exploration of information entropy and statistical measures, including Shannon entropy, relative entropy, maximum entropy (maxent), and mutual information A practical discussion of ad hoc, ab initio, and bootstrap signal acquisition methods, with examples from genome analytics and signal analytics Perfect for undergraduate and graduate students in machine learning and data analytics programs, Informatics and Machine Learning: From Martingales to Metaheuristics will also earn a place in the libraries of mathematicians, engineers, computer scientists, and life scientists with an interest in those subjects.

Network models are critical tools in business, management, science and industry. "Network Models and Optimization" presents an insightful, comprehensive, and up-to-date treatment of multiple objective genetic algorithms to network optimization problems in many disciplines, such as engineering, computer science, operations research, transportation, telecommunication, and manufacturing. The book extensively covers algorithms and applications, including shortest path problems, minimum cost flow problems, maximum flow problems, minimum spanning tree problems, traveling salesman and postman problems, location-allocation problems, project scheduling problems, multistage-based scheduling problems, logistics network problems, communication network problem, and network models in assembly line balancing problems, and airline fleet assignment problems. The book can be used both as a student textbook and as a professional reference for practitioners who use network optimization methods to model and solve problems.

This LNCS volume contains the papers presented at SEAL 2008, the 7th Int- nationalConference on Simulated Evolutionand Learning, held December 7-10, 2008, in Melbourne, Australia. SEAL is a prestigious international conference series in evolutionary computation and learning. This biennial event was ?rst held in Seoul, Korea, in 1996, and then in Canberra, Australia (1998), Nagoya, Japan (2000), Singapore (2002), Busan, Korea (2004), and Hefei, China (2006). SEAL 2008 received 140 paper submissions from more than 30 countries. After a rigorous peer-review process involving at least 3 reviews for each paper (i.e., over 420 reviews in total), the best 65 papers were selected to be presented at the conference and included in this volume, resulting in an acceptance rate of about 46%. The papers included in this volume cover a wide range of topics in simulated evolution and learning: from evolutionarylearning to evolutionary optimization, from hybrid systems to adaptive systems, from theoretical issues to real-world applications. They represent some of the latest and best research in simulated evolution and learning in the world

VolumeVIIIoftheTransactions on Rough Sets (TRS)containsa widespectrum of contributions to the theory and applications of rough sets. The pioneering work by Prof. Zdzis law Pawlak led to the introduction of knowledge representation systems during the early 1970s and the discovery of rough sets during the early 1980s. During his lifetime, he nurtured worldwide interest in approximation, approximate reasoning, and rough set theory and its 1 applications . Evidence of the in?uence of Prof. Pawlak's work can be seen in the growth in the rough-set literature that now includes over 4000 publications 2 by more than 1900 authors in the rough set database as well as the growth and 3 maturity of the International Rough Set Society . This volume of TRS presents papers that introduce a number of new - vances in the foundations and applications of arti?cial intelligence, engineering, logic, mathematics, and science. These advances have signi?cant implications in a number of researchareas.In addition, it is evident from the papers included in this volume that roughset theoryand its application forma veryactiveresearch area worldwide. A total of 58 researchers from 11 countries are represented in this volume, namely, Australia, Canada, Chile, Germany, India, Poland, P.R. China, Oman, Spain, Sweden, and the USA. Evidence of the vigor, breadth, and depth of research in the theory and applications rough sets can be found in the articles in this volume. This volume contains 17 papers that explore a number of research streams.

In machine learning applications, practitioners must take into account the cost associated with the algorithm. These costs include: Cost of acquiring training data Cost of data annotation/labeling and cleaning Computational cost for model fitting, validation, and testing Cost of collecting features/attributes for test data Cost of user feedback collection Cost of incorrect prediction/classification Cost-Sensitive Machine Learning is one of the first books to provide an overview of the current research efforts and problems in this area. It discusses real-world applications that incorporate the cost of learning into the modeling process. The first part of the book presents the theoretical underpinnings of cost-sensitive machine learning. It describes well-established machine learning approaches for reducing data acquisition costs during training as well as approaches for reducing costs when systems must make predictions for new samples. The second part covers real-world applications that effectively trade off different types of costs. These applications not only use traditional machine learning approaches, but they also incorporate cutting-edge research that advances beyond the constraining assumptions by analyzing the application needs from first principles. Spurring further research on several open problems, this volume highlights the often implicit assumptions in machine learning techniques that were not fully understood in the past. The book also illustrates the commercial importance of cost-sensitive machine learning through its coverage of the rapid application developments made by leading companies and academic research labs.

This book constitutes the thoroughly refereed post-workshop proceedings of the 2008 Pacific Rim Knowledge Acquisition Workshop, PKAW 2008, held in Hanoi, Vietnam, in December 2008 as part of 10th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2008.

The 20 revised papers presented were carefully reviewed and selected from 57 submissions and went through two rounds of reviewing and improvement. The papers are organized in topical sections on machine learning and data mining, incremental knowledge acquisition, web-based techniques and applications, as well as domain specific knowledge acquisition methods and applications.

This book constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2008, held in Antwerp, Belgium, in September 2008.

The 100 papers presented in two volumes, together with 5 invited talks, were carefully reviewed and selected from 521 submissions. In addition to the regular papers the volume contains 14 abstracts of papers appearing in full version in the Machine Learning Journal and the Knowledge Discovery and Databases Journal of Springer.

The conference intends to provide an international forum for the discussion of the latest high quality research results in all areas related to machine learning and knowledge discovery in databases. The topics addressed are application of machine learning and data mining methods to real-world problems, particularly exploratory research that describes novel learning and mining tasks and applications requiring non-standard techniques.

Volume IX of the Transactions on Rough Sets (TRS) provides evidence of the continuing growth of a number of research streams that were either directly or indirectly begun by the seminal work on rough sets by Zdzis law Pawlak (1926- 1 2006) .OneoftheseresearchstreamsinspiredbyProf.Pawlakisroughset-based intelligent systems, a topic that was an important part of his early 1970s work on knowledge description systems prior to his discovery of rough sets during the early 1980s. Evidence of intelligent systems as a recurring motif over the past twodecadescanbefoundintherough-setliteraturethatnowincludesover4,000 2 publications by more than 1,600 authors in the rough set database . This volume of the TRS includes articles that are extensions of papers in- 3 cludedinthe?rstconferenceonRoughSetsandIntelligentSystemsParadigms . In addition to research on intelligent systems, this volume also presents papers that re?ect the profound in?uence of a number of other research initiatives by Zdzis law Pawlak. In particular, this volume introduces a number of new advances in the fo- dations and applications of arti?cial intelligence, engineering, image processing, logic, mathematics, medicine, music, and science. These advances have sign- icant implications in a number of research areas such as attribute reduction, approximation schemes, category-based inductive reasoning, classi?ers, classi- ing mappings, context algebras, data mining, decision attributes, decision rules, decision support, diagnostic feature analysis, EEG classi?cation, feature ana- sis, granular computing, hierarchical classi?ers, indiscernibility relations, inf- mationgranulation, informationsystems, musicalrhythm retrieval, probabilistic dependencies, reducts, rough-fuzzy C-means, rough inclusion functions, rou- ness, singing voice recognition, and vagueness. A total of 47 researchers are represented in this volu

This book constitutes the refereed proceedings of the Workshop on Web Mining, WebMine 2006, held in Berlin, Germany, September 2006. Topics included are data mining based on analysis of bloggers and tagging, web mining, XML mining and further techniques of knowledge discovery. The book is especially valuable for those interested in the aspects of Web 2.0 and its inherent dynamic and diversity of user-generated content.