暂无相关内容,正在全力查找中
沃新书屋 -
Knowledge Representation and Relation Nets -
作者:Geldenhuys, Aletta E.; Van Rooyen, Hendrik O.; Stetter, Franz
Geldenhuys, Aletta E.; Van Rooyen, Hendrik O.; Stetter, Franz
人物简介:
Knowledge Representation and Relation Nets书籍相关信息
- ISBN:9780792385172
- 作者:Geldenhuys, Aletta E.; Van Rooyen, Hendrik O.; Stetter, Franz
- 出版社:暂无出版社
- 出版时间:1999-5
- 页数:290
- 价格:$ 258.77
- 纸张:暂无纸张
- 装帧:暂无装帧
- 开本:暂无开本
- 语言:暂无语言
- 适合人群:researchers in computer science, artificial intelligence professionals, machine learning engineers, data scientists, computer engineers, students studying computer science and related fields
- TAG:Machine Learning / Artificial Intelligence / Neural Networks / Knowledge Representation / graph neural networks / data mining
- 豆瓣评分:暂无豆瓣评分
- 更新时间:2025-05-08 01:21:38
内容简介:
Knowledge Representation and Relation Nets introduces a fresh approach to knowledge representation that can be used to organize study material in a convenient, teachable and learnable form. The method extends and formalizes concept mapping by developing knowledge representation as a structure of concepts and the relationships among them. Such a formal description of analogy results in a controlled method of modeling 'new' knowledge in terms of 'existing' knowledge in teaching and learning situations, and its applications result in a consistent and well-organized approach to problem solving. Additionally, strategies for the presentation of study material to learners arise naturally in this representation. While the theory of relation nets is dealt with in detail in part of this book, the reader need not master the formal mathematics in order to apply the theory to this method of knowledge representation. To assist the reader, each chapter starts with a brief summary, and the main ideas are illustrated by examples. The reader is also given an intuitive view of the formal notions used in the applications by means of diagrams, informal descriptions, and simple sets of construction rules. Knowledge Representation and Relation Nets is an excellent source for teachers, courseware designers and researchers in knowledge representation, cognitive science, theories of learning, the psychology of education, and structural modeling.