machine learning based approaches (9 Ergebnisse)

Taschenbuch. Zustand: Neu. Query Based Text Summarization using Machine learning Approach | Learning Approaches | Zarah Zainab (u. a.) | Taschenbuch | 80 S. | Englisch | 2019 | LAP LAMBERT Academic Publishing | EAN 9786139452828 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Taschenbuch. Zustand: Neu. Emotions Detection in Music Lyrics | Using Machine Learning and Keyword-Based Approaches | Ricardo Malheiro | Taschenbuch | Englisch | 2018 | Novas Edições Acadêmicas | EAN 9786202188548 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Zustand: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | Extraction of relevant information on a specific query from rapidly growing data is a concern for quiet time in order to scan and analyze data from all the related documents. Therefore, text summarization is paramount research area these days. It is about to find most relevant information from single or multi-documents. A reasonable amount of work is done in this area to overcome extensive searching and to reduce the time required. The knowledge-based and machine learning are the two methods for query-based text summarization where Machine learning approaches are mostly used for calculating probabilistic feature using Natural Language Processing (NLP) tools and techniques for both supervised and unsupervised learning. In the first part of this research work include to identify and analyze machine learning approaches for query-based text summarization for finding a useful summary for the users as specified by their need. In the second part, a comprehensive discussion is done to present the internal working mechanism of machine learning approaches for query-based text summarization.

Zustand: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | Extraction of relevant information on a specific query from rapidly growing data is a concern for quiet time in order to scan and analyze data from all the related documents. Therefore, text summarization is paramount research area these days. It is about to find most relevant information from single or multi-documents. A reasonable amount of work is done in this area to overcome extensive searching and to reduce the time required. The knowledge-based and machine learning are the two methods for query-based text summarization where Machine learning approaches are mostly used for calculating probabilistic feature using Natural Language Processing (NLP) tools and techniques for both supervised and unsupervised learning. In the first part of this research work include to identify and analyze machine learning approaches for query-based text summarization for finding a useful summary for the users as specified by their need. In the second part, a comprehensive discussion is done to present the internal working mechanism of machine learning approaches for query-based text summarization.

Zustand: New.

Zustand: New. Anirban Mukherjee has a Bachelors in Civil Engineering (1994) from Jadavpur University, Kolkata and PhD from Indian Institute of Engineering, Science and Technology (IIEST), Shibpur, India. He has been a Professor in the Department of Information .

Hardcover. Zustand: Brand New. 268 pages. 9.18x6.12x9.21 inches. In Stock.

Zustand: Good.

Buch. Zustand: Neu. Neuware - The use of intelligent technologies to enhance instruction and learning is introduced in pedagogy-based learning-teaching perspective. It covers digital library resources, AI-based tools, data analysis techniques, and NLP and NLU-powered smart assistants. Students will realize their improved efficacy through use of expandable AI systems improve educational efficiency, automate repetitive chores, and enable personalized learning. The course offers useful skills for implementing contemporary AI methods in educational institutions, classrooms, and online learning settings.