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Learning from Imbalanced Data Sets
This book provides a general and comprehensible overview of imbalanced learning. It contains a formal description of a problem, and focuses on its main features, and the most relevant proposed solutions. Additionally, it considers the different scenarios in Data Science for which the imbalanced classification can create a real challenge. This book stresses the gap with standard classification tasks by reviewing the case studies and ad-hoc performance metrics that are applied in this area. It also covers the different approaches that have been traditionally applied to address the binary skewed class distribution. Specifically, it reviews cost-sensitive learning, data-level preprocessing metho...
Nonlinear Estimation and Classification
Researchers in many disciplines face the formidable task of analyzing massive amounts of high-dimensional and highly-structured data. This is due in part to recent advances in data collection and computing technologies. As a result, fundamental statistical research is being undertaken in a variety of different fields. Driven by the complexity of these new problems, and fueled by the explosion of available computer power, highly adaptive, non-linear procedures are now essential components of modern "data analysis," a term that we liberally interpret to include speech and pattern recognition, classification, data compression and signal processing. The development of new, flexible methods combines advances from many sources, including approximation theory, numerical analysis, machine learning, signal processing and statistics. The proposed workshop intends to bring together eminent experts from these fields in order to exchange ideas and forge directions for the future.
Machine Learning: Theory and Applications
Statistical learning and analysis techniques have become extremely important today, given the tremendous growth in the size of heterogeneous data collections and the ability to process it even from physically distant locations. Recent advances made in the field of machine learning provide a strong framework for robust learning from the diverse corpora and continue to impact a variety of research problems across multiple scientific disciplines. The aim of this handbook is to familiarize beginners as well as experts with some of the recent techniques in this field.The Handbook is divided in two sections: Theory and Applications, covering machine learning, data analytics, biometrics, document recognition and security. - Very relevant to current research challenges faced in various fields - Self-contained reference to machine learning - Emphasis on applications-oriented techniques
Detour Interior and Boundary vertices of BSV Neutrosophic Graphs
In the present article, we deduce a characterization of BSVN detour interior and boundary vertices. We established the relations between BSVN cut node and BSVN detour boundary nodes. Further, we studied properties of BSVN boundary nodes and BSVN interior nodes. Application of detour boundary node, detour interior is given on modeling wireless sensor network in terms of BSVN graphs.
Vedic Cosmography and Astronomy
The mysteries of the fifth Canto of the Srimad Bhagavatam have long puzzled students of Vedic cosmography and astronomy. Confronted with a description of the universe that seems much at variance with the information provided by our senses and standard astronomical calculations, foreign observers and even Indian commentators from the middle ages up to the present have concluded that the Bhagavatam`s account elaborated in other Puranas must be mythological. On the other hand the same persons have been much impressed with vedic astronomical treatises the jyotisasastras which provide remarkably accurate measurements of the solar system.
C4.5
This book is a complete guide to the C4.5 system as implemented in C for the UNIX environment. It contains a comprehensive guide to the system's use, the source code (about 8,800 lines), and implementation notes.
Introduction To The Theory Of Neural Computation
Comprehensive introduction to the neural network models currently under intensive study for computational applications. It also provides coverage of neural network applications in a variety of problems of both theoretical and practical interest.
Political Analysis
Discusses some of the latest developments in political methodology
