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In den WarenkorbPaperback. Zustand: Very Good. Data Mining with Neural Networks: Solving Business Problems from Application Development to Decision Support This book is in very good condition and will be shipped within 24 hours of ordering. The cover may have some limited signs of wear but the pages are clean, intact and the spine remains undamaged. This book has clearly been well maintained and looked after thus far. Money back guarantee if you are not satisfied. See all our books here, order more than 1 book and get discounted shipping.
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Sprache: Englisch
Verlag: Financial Times/ Prentice Hall, 1998
ISBN 10: 0273632698 ISBN 13: 9780273632696
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Soft cover. Zustand: Near Fine. 8vo (23 cm), XVII, 222 pp. Laminated wrappers (minor shelf-wear). This book provides an in-depth understanding of the principles, techniques, and applications of using neural networks for data mining purposes. Bigus guides readers through the process of leveraging neural networks to extract valuable insights and patterns from complex datasets. From the basics of neural network architecture to advanced topics such as training algorithms and model evaluation, this book offers a thorough exploration of the subject. With practical examples and case studies, "Data Mining With Neural Networks" demonstrates how neural networks can be applied to solve real-world data mining problems. Whether you are a beginner or an experienced practitioner, this book serves as a valuable resource for understanding and harnessing the power of neural networks in the context of data mining.
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Sprache: Englisch
Verlag: LAP LAMBERT Academic Publishing, 2019
ISBN 10: 6139920140 ISBN 13: 9786139920143
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Sprache: Englisch
Verlag: LAP LAMBERT Academic Publishing, 2019
ISBN 10: 6139920140 ISBN 13: 9786139920143
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In den WarenkorbPaperback. Zustand: Brand New. 84 pages. 8.66x5.91x0.19 inches. In Stock.
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In den WarenkorbPaperback. Zustand: Brand New. 308 pages. 8.75x6.00x0.75 inches. In Stock.
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Sprache: Englisch
Verlag: LAP LAMBERT Academic Publishing Jul 2019, 2019
ISBN 10: 6139920140 ISBN 13: 9786139920143
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Taschenbuch. Zustand: Neu. Neuware -Medical decision support system (MDSS) are now being used in many health care institutions across the glove, these institutions have large amount of medical data stored in different format and may contain relevant data that are hidden. The use of data mining is to extract hidden knowledge from a relevant data, that is why the main aim of this book is to show how data mining methods can be applied in medical decision support system and also to design a web based expert system that can predict heart condition using neural network. The design of the system is based on VA Medical center long beach database and collected from the UCI machine learning repository. After analyzing several medical decision support systems in the relevant literature, three algorithms have been identified: multilayer perceptron, decision tree and Naïve Bayes. These algorithms are tested under different configuration in order to find the best on the two medical dataset. Thereafter, a comparison was made with respect to their performance based on some set of performance metrics. The analysis was done using WEKA on the two medical dataset which are diabetes and heart diseases database.Books on Demand GmbH, Überseering 33, 22297 Hamburg 84 pp. Englisch.
Zustand: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.
Anbieter: Romtrade Corp., STERLING HEIGHTS, MI, USA
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Sprache: Englisch
Verlag: LAP LAMBERT Academic Publishing, 2019
ISBN 10: 6139920140 ISBN 13: 9786139920143
Anbieter: preigu, Osnabrück, Deutschland
Taschenbuch. Zustand: Neu. Application of data mining in medical decision support systems | Habib Shariff Mahamud | Taschenbuch | 84 S. | Englisch | 2019 | LAP LAMBERT Academic Publishing | EAN 9786139920143 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.
Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Doctoral Thesis / Dissertation from the year 2020 in the subject Computer Science - Commercial Information Technology, Symbiosis International University, language: English, abstract: Data mining is coined one of the steps while discovering insights from large amounts of data which may be stored in databases, data warehouses, or in other information repositories. Data mining is now playing a significant role in seeking a decision support to draw higher profits by the modern business world. Various researchers studied the benefits of data mining processes and its adoption by business organizations, but very few of them have discussed the success factors of decision support projects. The Research Hypothesis states the involvement of the decision tree while adopting accuracy of classification and while emphasizing the impact factor or importance of the attributes rather than the information gain. The concept of involvement of impact factor rather than just accuracy can be utilized in developing the new algorithm whose performance improves over the existing algorithms. We proposed a new algorithm which improves accuracy and contributing effectively in decision tree learning. We presented an algorithm that resolves the above stated problem of confliction of class. We have introduced the impact factor and classified impact factor to resolve the conflict situation. We have used data mining technique in facilitating the decision support with improved performance over its existing companion. We have also addressed the unique problem which have not been addressed before. Definitely, the fusion of data mining and decision support can contribute to problem-solving by enabling the vast hidden knowledge from data and knowledge received from experts. We have discussed a lot of work done in the field of decision support and hierarchical multi-attribute decision models. Ample amount of algorithms are available which are used to classify the data in datasets. Most algorithms use the concept of information gain for classification purpose. Some Lacking areas also exist. There is a need for an ideal algorithm for large datasets. There is a need for handling the missing values. There is a need for removing attribute bias towards choosing a random class when a conflict occurs. There is a need for decision support model which takes the advantages of hierarchical multi-attribute classification algorithms.
Sprache: Englisch
Verlag: GRIN Verlag, GRIN Verlag Jan 2021, 2021
ISBN 10: 3346292320 ISBN 13: 9783346292322
Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
Taschenbuch. Zustand: Neu. Neuware -Doctoral Thesis / Dissertation from the year 2020 in the subject Computer Science - Commercial Information Technology, Symbiosis International University, language: English, abstract: Data mining is coined one of the steps while discovering insights from large amounts of data which may be stored in databases, data warehouses, or in other information repositories. Data mining is now playing a significant role in seeking a decision support to draw higher profits by the modern business world. Various researchers studied the benefits of data mining processes and its adoption by business organizations, but very few of them have discussed the success factors of decision support projects. The Research Hypothesis states the involvement of the decision tree while adopting accuracy of classification and while emphasizing the impact factor or importance of the attributes rather than the information gain. The concept of involvement of impact factor rather than just accuracy can be utilized in developing the new algorithm whose performance improves over the existing algorithms. We proposed a new algorithm which improves accuracy and contributing effectively in decision tree learning. We presented an algorithm that resolves the above stated problem of confliction of class. We have introduced the impact factor and classified impact factor to resolve the conflict situation. We have used data mining technique in facilitating the decision support with improved performance over its existing companion. We have also addressed the unique problem which have not been addressed before. Definitely, the fusion of data mining and decision support can contribute to problem-solving by enabling the vast hidden knowledge from data and knowledge received from experts. We have discussed a lot of work done in the field of decision support and hierarchical multi-attribute decision models. Ample amount of algorithms are available which are used to classify the data in datasets. Most algorithms use the concept of information gain for classification purpose. Some Lacking areas also exist. There is a need for an ideal algorithm for large datasets. There is a need for handling the missing values. There is a need for removing attribute bias towards choosing a random class when a conflict occurs. There is a need for decision support model which takes the advantages of hierarchical multi-attribute classification algorithms. 140 pp. Englisch.