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| Author | : Vyom S. Jhaveri |
| Publisher | : |
| Total Pages | : 84 |
| Release | : 2017 |
| Genre | : Information storage and retrieval systems |
| ISBN | : |
| Author | : Nikki Pelchat |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2021 |
| Genre | : |
| ISBN | : |
Music recommendation systems have become popular in recent years with the increasing variety of music content being produced as well as the sheer size of digital music collections which are available at the touch of a finger. Large collections of digital music are commonly organized using genre labels. In addition, music genres are regularly used by recommendation systems to suggest new music to the listeners. The chore of classifying a large amount of music manually can be difficult and time consuming. It is for these reasons, the automatic classification of music by genre is a crucial task. The ability to automatically classify music by genre using machine learning can be quicker and arguably more accurate than doing it manually. Using neural networks for generic classification tasks is a well researched area within machine learning. In recent years, the classification of music by genre has become part of the same problem domain. Differences in song libraries, machine learning techniques, input formats, and types of neural networks implemented have all had varying levels of success. This thesis implements a convolutional neural network that classifies music by genre through the examination of spectrogram images. It concentrates on three specific types of spectrogram inputs (Linear, Logarithmic, and Mel scaled spectrograms) as well as several input variables and neural network learning techniques to determine the effect that they have on the overall accuracy of the genre classification network. This thesis demonstrates these convolutional neural network techniques for music genre classification and assesses their viability and accuracy.
| Author | : Abhishek Swaroop |
| Publisher | : Springer Nature |
| Total Pages | : 937 |
| Release | : 2023-09-16 |
| Genre | : Technology & Engineering |
| ISBN | : 9819937167 |
This book features high-quality research papers presented at Fourth Doctoral Symposium on Computational Intelligence (DoSCI 2023), organized by Institute of Engineering and Technology (IET), AKTU, Lucknow, India, on March 3, 2023. This book discusses the topics such as computational intelligence, artificial intelligence, deep learning, evolutionary algorithms, swarm intelligence, fuzzy sets and vague sets, rough set theoretic approaches, quantum-inspired computational intelligence, hybrid computational intelligence, machine learning, computer vision, soft computing, distributed computing, parallel and grid computing, cloud computing, high-performance computing, biomedical computing, and decision support and decision making
| Author | : Monica Bianchini |
| Publisher | : Springer Nature |
| Total Pages | : 649 |
| Release | : 2021-07-21 |
| Genre | : Technology & Engineering |
| ISBN | : 9811621640 |
This book gathers selected high-quality research papers presented at International Conference on Advanced Computing and Intelligent Technologies (ICACIT 2021) held at NCR New Delhi, India, during March 20–21, 2021, jointly organized by Galgotias University, India, and Department of Information Engineering and Mathematics Università Di Siena, Italy. It discusses emerging topics pertaining to advanced computing, intelligent technologies, and networks including AI and machine learning, data mining, big data analytics, high-performance computing network performance analysis, Internet of things networks, wireless sensor networks, and others. The book offers a valuable asset for researchers from both academia and industries involved in advanced studies.
| Author | : Yajie Hu |
| Publisher | : |
| Total Pages | : |
| Release | : 2012 |
| Genre | : |
| ISBN | : |
This thesis presents a new approach to recommend suitable tracks from a collection of songs to the user. The goal of the system is to recommend songs that are preferred by the user, are fresh to the user's ear, and fit the user's listening pattern. ``Forgetting Curve'' is used to assess freshness of a song and the user log is used to evaluate the preference. I analyze user's listening pattern to estimate the level of interest of the user in the next song. Also, user behavior is treated on the song being played as feedback to adjust the recommendation strategy for the next one. Furthermore, this thesis proposes a method to classify songs in the Million Song Dataset according to song genre. Since songs have several data types, several sub-classifiers are trained by different types of data. These sub-classifiers are combined using both classifier authority and classification confidence for a particular instance. In the experiments, the combined classifier surpasses all of these sub-classifiers and the SVM classifier using concatenated vectors from all data types. Finally, I develop an application to evaluate our approach in the real world.
| Author | : Francesco Ricci |
| Publisher | : Springer |
| Total Pages | : 1008 |
| Release | : 2015-11-17 |
| Genre | : Computers |
| ISBN | : 148997637X |
This second edition of a well-received text, with 20 new chapters, presents a coherent and unified repository of recommender systems’ major concepts, theories, methodologies, trends, and challenges. A variety of real-world applications and detailed case studies are included. In addition to wholesale revision of the existing chapters, this edition includes new topics including: decision making and recommender systems, reciprocal recommender systems, recommender systems in social networks, mobile recommender systems, explanations for recommender systems, music recommender systems, cross-domain recommendations, privacy in recommender systems, and semantic-based recommender systems. This multi-disciplinary handbook involves world-wide experts from diverse fields such as artificial intelligence, human-computer interaction, information retrieval, data mining, mathematics, statistics, adaptive user interfaces, decision support systems, psychology, marketing, and consumer behavior. Theoreticians and practitioners from these fields will find this reference to be an invaluable source of ideas, methods and techniques for developing more efficient, cost-effective and accurate recommender systems.
| Author | : Ian H. Witten |
| Publisher | : Morgan Kaufmann |
| Total Pages | : 655 |
| Release | : 2016-10-01 |
| Genre | : Computers |
| ISBN | : 0128043571 |
Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches. Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research. Please visit the book companion website at https://www.cs.waikato.ac.nz/~ml/weka/book.html. It contains Powerpoint slides for Chapters 1-12. This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the book Online Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the book Table of contents, highlighting the many new sections in the 4th edition, along with reviews of the 1st edition, errata, etc. Provides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projects Presents concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods Includes a downloadable WEKA software toolkit, a comprehensive collection of machine learning algorithms for data mining tasks-in an easy-to-use interactive interface Includes open-access online courses that introduce practical applications of the material in the book
| Author | : Dietmar Jannach |
| Publisher | : Cambridge University Press |
| Total Pages | : |
| Release | : 2010-09-30 |
| Genre | : Computers |
| ISBN | : 1139492594 |
In this age of information overload, people use a variety of strategies to make choices about what to buy, how to spend their leisure time, and even whom to date. Recommender systems automate some of these strategies with the goal of providing affordable, personal, and high-quality recommendations. This book offers an overview of approaches to developing state-of-the-art recommender systems. The authors present current algorithmic approaches for generating personalized buying proposals, such as collaborative and content-based filtering, as well as more interactive and knowledge-based approaches. They also discuss how to measure the effectiveness of recommender systems and illustrate the methods with practical case studies. The final chapters cover emerging topics such as recommender systems in the social web and consumer buying behavior theory. Suitable for computer science researchers and students interested in getting an overview of the field, this book will also be useful for professionals looking for the right technology to build real-world recommender systems.
| Author | : Charu C. Aggarwal |
| Publisher | : Springer |
| Total Pages | : 518 |
| Release | : 2016-03-28 |
| Genre | : Computers |
| ISBN | : 3319296590 |
This book comprehensively covers the topic of recommender systems, which provide personalized recommendations of products or services to users based on their previous searches or purchases. Recommender system methods have been adapted to diverse applications including query log mining, social networking, news recommendations, and computational advertising. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. The chapters of this book are organized into three categories: Algorithms and evaluation: These chapters discuss the fundamental algorithms in recommender systems, including collaborative filtering methods, content-based methods, knowledge-based methods, ensemble-based methods, and evaluation. Recommendations in specific domains and contexts: the context of a recommendation can be viewed as important side information that affects the recommendation goals. Different types of context such as temporal data, spatial data, social data, tagging data, and trustworthiness are explored. Advanced topics and applications: Various robustness aspects of recommender systems, such as shilling systems, attack models, and their defenses are discussed. In addition, recent topics, such as learning to rank, multi-armed bandits, group systems, multi-criteria systems, and active learning systems, are introduced together with applications. Although this book primarily serves as a textbook, it will also appeal to industrial practitioners and researchers due to its focus on applications and references. Numerous examples and exercises have been provided, and a solution manual is available for instructors.
| Author | : Christos Douligeris |
| Publisher | : Springer Nature |
| Total Pages | : 429 |
| Release | : 2019-08-21 |
| Genre | : Computers |
| ISBN | : 303029563X |
This two-volume set of LNAI 11775 and LNAI 11776 constitutes the refereed proceedings of the 12th International Conference on Knowledge Science, Engineering and Management, KSEM 2019, held in Athens, Greece, in August 2019. The 77 revised full papers and 23 short papers presented together with 10 poster papers were carefully reviewed and selected from 240 submissions. The papers of the first volume are organized in the following topical sections: Formal Reasoning and Ontologies; Recommendation Algorithms and Systems; Social Knowledge Analysis and Management ; Data Processing and Data Mining; Image and Video Data Analysis; Deep Learning; Knowledge Graph and Knowledge Management; Machine Learning; and Knowledge Engineering Applications. The papers of the second volume are organized in the following topical sections: Probabilistic Models and Applications; Text Mining and Document Analysis; Knowledge Theories and Models; and Network Knowledge Representation and Learning.
