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The Essentials of Machine Learning in Finance and Accounting
This book introduces machine learning in finance and illustrates how we can use computational tools in numerical finance in real-world context. These computational techniques are particularly useful in financial risk management, corporate bankruptcy prediction, stock price prediction, and portfolio management. The book also offers practical and managerial implications of financial and managerial decision support systems and how these systems capture vast amount of financial data. Business risk and uncertainty are two of the toughest challenges in the financial industry. This book will be a useful guide to the use of machine learning in forecasting, modeling, trading, risk management, economics, credit risk, and portfolio management.
Computational Intelligence for Oncology and Neurological Disorders
With the advent of computational intelligence-based approaches, such as bio-inspired techniques, and the availability of clinical data from various complex experiments, medical consultants, researchers, neurologists, and oncologists, there is huge scope for CI-based applications in medical oncology and neurological disorders. This book focuses on interdisciplinary research in this field, bringing together medical practitioners dealing with neurological disorders and medical oncology along with CI investigators. The book collects high-quality original contributions, containing the latest developments or applications of practical use and value, presenting interdisciplinary research and review articles in the field of intelligent systems for computational oncology and neurological disorders. Drawing from work across computer science, physics, mathematics, medical science, psychology, cognitive science, oncology, and neurobiology among others, it combines theoretical, applied, computational, experimental, and clinical research. It will be of great interest to any neurology or oncology researchers focused on computational approaches.
Kernel Methods and Machine Learning
Covering the fundamentals of kernel-based learning theory, this is an essential resource for graduate students and professionals in computer science.
The Road To Mecca
Part travelogue, part autobiography, "The Road to Mecca" is the compelling story of a Western journalist and adventurer who converted to Islam in the early twentieth century. A spiritual and literary counterpart of Wilfred Thesiger and a contemporary of T. E. Lawrence (Lawrence of Arabia), Muhammad Asad journeyed around the Middle East, Afghanistan and India. This is an account of Asad's adventures in Arabia, his inner awakening, and his relationships with nomads and royalty alike, set in the wake of the First World War. It can be read on many levels: as a eulogy to a lost world, and as the poignant account of a man's search for meaning. It is also a love story, defying convention and steeped in loss. With its evocative descriptions and profound insights on the Islamic world, "The Road to Mecca" is a work of immense value today.
Modern Multivariate Statistical Techniques
This is the first book on multivariate analysis to look at large data sets which describes the state of the art in analyzing such data. Material such as database management systems is included that has never appeared in statistics books before.
統計数理硏究所年報
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Pervasive Social Computing
This book presents a review of traditional context-aware computing research, identifies its limitations in developing social context-aware pervasive systems, and introduces a new technology framework to address these limitations. Thus, this book provides a good reference for developments in context-aware computing and pervasive social computing. It examines the emerging area of pervasive social computing, which is a novel collective paradigm derived from pervasive computing, social media, social networking, social signal processing and multimodal human-computer interaction. This book offers a novel approach to model, represent, reason about and manage different types of social context. It sh...
Metalearning
Metalearning is the study of principled methods that exploit metaknowledge to obtain efficient models and solutions by adapting machine learning and data mining processes. While the variety of machine learning and data mining techniques now available can, in principle, provide good model solutions, a methodology is still needed to guide the search for the most appropriate model in an efficient way. Metalearning provides one such methodology that allows systems to become more effective through experience. This book discusses several approaches to obtaining knowledge concerning the performance of machine learning and data mining algorithms. It shows how this knowledge can be reused to select, combine, compose and adapt both algorithms and models to yield faster, more effective solutions to data mining problems. It can thus help developers improve their algorithms and also develop learning systems that can improve themselves. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining and artificial intelligence.
