Knowledge Transfer Between Computer Vision And Text Mining
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Knowledge Transfer between Computer Vision and Text Mining
This ground-breaking text/reference diverges from the traditional view that computer vision (for image analysis) and string processing (for text mining) are separate and unrelated fields of study, propounding that images and text can be treated in a similar manner for the purposes of information retrieval, extraction and classification. Highlighting the benefits of knowledge transfer between the two disciplines, the text presents a range of novel similarity-based learning (SBL) techniques founded on this approach. Topics and features: describes a variety of SBL approaches, including nearest neighbor models, local learning, kernel methods, and clustering algorithms; presents a nearest neighbor model based on a novel dissimilarity for images; discusses a novel kernel for (visual) word histograms, as well as several kernels based on a pyramid representation; introduces an approach based on string kernels for native language identification; contains links for downloading relevant open source code.
Digital Personality: A Man Forever
The book explores the creation of digital personalities that mimic human behaviour and cognition, authored by AI and computer science experts. It covers the technical foundations needed to develop advanced digital personas, focusing on the integration of ontologies, natural language processing (NLP), and dialogue generation. Ontologies are highlighted for their role in structuring knowledge, while NLP techniques are explored for enabling human-like dialogue. The book examines algorithms for sentiment analysis, entity recognition, and context understanding. Dialogue generation is also discussed, from rule-based methods to deep learning, emphasizing seamless user interactions. Ethical concerns, such as privacy, bias, and accountability, are addressed, advocating for responsible AI practices. This volume is a comprehensive resource for researchers and enthusiasts, offering both theoretical insights and practical guidance on building lifelike digital entities and fostering emotionally engaging human-computer interactions.
Introduction to Statistical Computing and Visualization Using R
The book provides a foundational guide to statistical computing and visualisation Using R programming with an emphasis on practical data analysis skills that are directly applicable to diverse fields like finance, defence, health, and education. It uniquely combines a thorough explanation of basic constructs with advanced topics such as data visualisation, statistical modeling, and probability, making it accessible yet comprehensive for learners across disciplines. This approach allows readers not only to build essential R skills but also to apply them to real-world scenarios, equipping students and professionals from various disciplines with versatile analytical tools. It offers a comprehensive yet approachable introduction for students and scholars from various disciplines using R. Includes practical and interactive elements such as quizzes, coding exercises, and hands-on projects can provide an engaging and effective learning experience for readers Provides complete code solutions to every problem presented, including detailed answers to even the most complex questions Presents case studies that can help contextualize the concepts covered in the book by showing how they are used in specific industries, fields, or contexts Offers application-based practical data analysis with cases in various fields and sectors, such as finance, healthcare, and marketing Focuses on best practices and efficient coding techniques, improving productivity and maintainability of R code