| Machine Learning and Data Mining in Pattern Recognition: First International Workshop, Mldm'99, Leipzig, Germany, September 16-18, 1999, Proceedings 1999 Edition Contributor(s): Perner, Petra (Editor), Petrou, Maria (Editor) |
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ISBN: 3540665994 ISBN-13: 9783540665991 Publisher: Springer OUR PRICE: $52.24 Product Type: Paperback - Other Formats Published: September 1999 Annotation: This book constitutes the refereed proceedings of the First International Workshop on Machine Learning and Data Mining in Pattern Recognition, MLDM'99, held in Leipzig, Germany in September 1999. The 15 revised full papers presented together with two invited contributions were carefully reviewed. The papers are organized in sections on neural networks applied to image processing and recognition, learning in image pre-processing and segmentation, image retrieval, classification and image interpretation, symbolic learning and neural networks in document processing, and data mining. |
| Additional Information |
| BISAC Categories: - Medical - Computers | Image Processing - Computers | Computer Vision & Pattern Recognition |
| Dewey: 006.31 |
| LCCN: 99047725 |
| Series: Lecture Notes in Computer Science |
| Physical Information: 0.49" H x 6.14" W x 9.21" (0.73 lbs) 224 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: The field of machine learning and data mining in connection with pattern recognition enjoys growing popularity and attracts many researchers. Automatic pattern recognition systems have proven successful in many applications. The wide use of these systems depends on their ability to adapt to changing environmental conditions and to deal with new objects. This requires learning capabilities on the parts of these systems. The exceptional attraction of learning in pattern recognition lies in the specific data themselves and the different stages at which they get processed in a pattern recognition system. This results a specific branch within the field of machine learning. At the workshop, were presented machine learning approaches for image pre-processing, image segmentation, recognition and interpretation. Machine learning systems were shown on applications such as document analysis and medical image analysis. Many databases are developed that contain multimedia sources such as images, measurement protocols, and text documents. Such systems should be able to retrieve these sources by content. That requires specific retrieval and indexing strategies for images and signals. Higher quality database contents can be achieved if it were possible to mine these databases for their underlying information. Such mining techniques have to consider the specific characteristic of the image sources. The field of mining multimedia databases is just starting out. We hope that our workshop can attract many other researchers to this subject. |