Knowledge Representation and Organization in Machine Learning
| Author/creator | Morik, K. Author |
| Format | Electronic |
| Publication Info | New York : Springer |
| Description | 340 p. 09.210 x 06.140 in. |
| Supplemental Content | Full text available from Springer Books |
| Supplemental Content | Full text available from SpringerLINK Lecture Notes in Computer Science |
| Subjects |
| Series | Lecture Notes in Computer Science Ser. |
| Summary | Annotation Machine learning has become a rapidly growing field of Artificial Intelligence. Since the First International Workshop on Machine Learning in 1980, the number of scientists working in the field has been increasing steadily. This situation allows for specialization within the field. There are two types of specialization: on subfields or, orthogonal to them, on special subjects of interest. This book follows the thematic orientation. It contains research papers, each of which throws light upon the relation between knowledge representation, knowledge acquisition and machine learning from a different angle. Building up appropriate representations is considered to be the main concern of knowledge acquisition for knowledge-based systems throughout the book. Here machine learning is presented as a tool for building up such representations. But machine learning itself also states new representational problems. This book gives an easy-to-understand insight into a new field with its problems and the solutions it offers. Thus it will be of good use to both experts and newcomers to the subject. |
| Access restriction | Available only to authorized users. |
| Technical details | Mode of access: World Wide Web |
| Genre/form | Electronic books. |
| ISBN | 9783540507680 |
| ISBN | 354050768X (Perfect) Active Record |
| Standard identifier# | 9783540507680 |
| Stock number | 00024965 |
Availability
| Library | Location | Call Number | Status | Item Actions |
|---|---|---|---|---|
| Electronic Resources | Access Content Online | ✔ Available |