Inicio > > Redes y comunicaciones informáticas > Machine Learning for Text
Machine Learning for Text

Machine Learning for Text

Charu C. Aggarwal

83,40 €
IVA incluido
Disponible
Editorial:
Springer Nature B.V.
Año de edición:
2023
Materia
Redes y comunicaciones informáticas
ISBN:
9783030966256
83,40 €
IVA incluido
Disponible

Selecciona una librería:

  • Librería Samer Atenea
  • Librería Aciertas (Toledo)
  • Kálamo Books
  • Librería Perelló (Valencia)
  • Librería Elías (Asturias)
  • Donde los libros
  • Librería Kolima (Madrid)
  • Librería Proteo (Málaga)
Sobre IBD

This second edition textbook covers a coherently organized framework for text analytics, which integrates material drawn from the intersecting topics of information retrieval, machine learning, and natural language processing. Particular importance is placed on deep learning methods. The chapters of this book span three broad categories:1. Basic algorithms: Chapters 1 through 7 discuss the classical algorithms for text analytics such as preprocessing, similarity computation, topic modeling, matrix factorization, clustering, classification, regression, and ensemble analysis.2. Domain-sensitive learning and information retrieval: Chapters 8 and 9 discuss learning models in heterogeneous settings such as a combination of text with multimedia or Web links. The problem of information retrieval and Web search is also discussed in the context of its relationship with ranking and machine learning methods. 3. Natural language processing: Chapters 10 through 16 discuss various sequence-centric and natural language applications, such as feature engineering, neural language models, deep learning, transformers, pre-trained language models, text summarization, information extraction, knowledge graphs, question answering, opinion mining, text segmentation, and event detection. Compared to the first edition, this second edition textbook (which targets mostly advanced level students majoring in computer science and math) has substantially more material on deep learning and natural language processing. Significant focus is placed on topics like transformers, pre-trained language models, knowledge graphs, and question answering.

Artículos relacionados

  • Next Generation Search Engines
    Recent technological progress in computer science, Web technologies, and the constantly evolving information available on the Internet has drastically changed the landscape of search and access to information. Current search engines employ advanced techniques involving machine learning, social networks, and semantic analysis. Next Generation Search Engines: Advanced Models for ...
  • Collaboration and the Semantic Web
    Collaborative working has been increasingly viewed as a good practice for organizations to achieve efficiency. Organizations that work well in collaboration may have access to new sources of funding, deliver new, improved, and more integrated services, make savings on shared costs, and exchange knowledge, information and expertise. Collaboration and the Semantic Web: Social Net...
  • Resource Allocation in Next-Generation Broadband Wireless Access Networks
    With the growing popularity of wireless networks in recent years, the need to increase network capacity and efficiency has become more prominent in society. This has led to the development and implementation of heterogeneous networks. Resource Allocation in Next-Generation Broadband Wireless Access Networks is a comprehensive reference source for the latest scholarly research o...
  • Advanced Topics in Information Technology Standards and Standardization Research, Volume 1
    Kai Jakobs
    ...
  • Data Warehouses and OLAP
    ...
  • Selected Readings on Database Technologies and Applications
    Terry Halpin
    Education and research in the field of database technology can prove problematic without the proper resources and tools on the most relevant issues, trends, and advancements. Selected Readings on Database Technologies and Applications supplements course instruction and student research with quality chapters focused on key issues concerning the development, design, and analysis ...

Otros libros del autor

  • Probability and Statistics for Machine Learning
    Charu C. Aggarwal
    This book covers probability and statistics from the machine learning perspective. The chapters of this book belong to three categories:1. The basics of probability and statistics: These chapters focus on the basics of probability and statistics, and cover the key principles of these topics. Chapter 1 provides an overview of the area of probability and statistics as well as its...
  • Neural Networks and Deep Learning
    Charu C. Aggarwal
    This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks wor...
  • Neural Networks and Deep Learning
    Charu C. Aggarwal
    This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks wor...
    Disponible

    50,49 €

  • Artificial Intelligence
    Charu C. Aggarwal
    This textbook covers the broader field of artificial intelligence.   The chapters for this textbook span within three categories:Deductive reasoning methods: These methods start with pre-defined hypotheses and reason with them in order to arrive at logically sound conclusions. The underlying methods include search and logic-based methods. These methods are discussed in Chapters...
    Disponible

    92,50 €

  • Machine Learning for Text
    Charu C. Aggarwal
    This second edition textbook covers a coherently organized framework for text analytics, which integrates material drawn from the intersecting topics of information retrieval, machine learning, and natural language processing. Particular importance is placed on deep learning methods. The chapters of this book span three broad categories:1. Basic algorithms: Chapters 1 through 7...
  • Machine Learning for Text
    Charu C. Aggarwal
    This second edition textbook covers a coherently organized framework for text analytics, which integrates material drawn from the intersecting topics of information retrieval, machine learning, and natural language processing. Particular importance is placed on deep learning methods. The chapters of this book span three broad categories:1. Basic algorithms: Chapters 1 through 7...
    Disponible

    50,62 €