Download Case-Based Reasoning Research and Development: 4th by Ralph Bergmann (auth.), David W. Aha, Ian Watson (eds.) PDF

By Ralph Bergmann (auth.), David W. Aha, Ian Watson (eds.)

The 2001 foreign convention on Case-Based Reasoning (ICCBR 2001, www.iccbr.org/iccbr01), the fourth within the biennial ICCBR sequence (1995 in Sesimbra, Portugal; 1997 in windfall, Rhode Island (USA); 1999 in Seeon, Germany), was once held in the course of 30 July – 2 August 2001 in Vancouver, Canada. ICCBR is the most advantageous foreign discussion board for researchers and practitioners of case dependent reasoning (CBR). The goals of this assembly have been to nurture major, correct advances made during this box (both in examine and application), converse them between all attendees, encourage destiny advances, and proceed to aid the imaginative and prescient that CBR is a useful method in lots of study disciplines, either computational and another way. ICCBR 2001 used to be the 1st ICCBR assembly hung on the Pacific coast, and we used the environment of gorgeous Vancouver as a chance to augment participation from the Pacific Rim groups, which contributed 28% of the submissions. in this assembly, we have been lucky to host invited talks through Ralph Bergmann, Ken Forbus, Jaiwei Han, Ramon López de Mántaras, and Manuela Veloso. Their contributions ensured a stimulating assembly; we thank them all.

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Additional info for Case-Based Reasoning Research and Development: 4th International Conference on Case-Based Reasoning, ICCBR 2001 Vancouver, BC, Canada, July 30 – August 2, 2001 Proceedings

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This approach is sufficiently flexible to be used for other applications and will therefore be used for Hep-2 cell image analysis. 2 Feature Selection Feature selection is concerned with learning the most important (symbolic) features, while feature extraction is responsible for locating those features in the image and finding their values. From the preprocessed, segmented, and labeled 1-D, 2-D, or 3-D image matrix we can extract low-level or primitive image features that are corners, extended edges, textured regions, ribbons, the 2 1/2-D sketch, and semantic clusters of edges and regions.

In S. ) Advances in Pattern Recognition (pp. 45-54). Berlin: Springer Verlag. Perner, P. (1999). An architecture for a CBR image segmentation system. -D. Althoff, R. Bergmann, & L. K. ) Case-Based Reasoning Research and Development (pp. 525-535). Berlin: Springer Verlag. Zamperoni, P. & Starovoitov, V. (1995). How dissimilar are two gray-scale images? 448-455). Berlin: Springer Verlag. Santini, S. & Jain, R. (1999). Similarity measures IEEE Transactions on Pattern Analysis and Machine Intelligence,.

That have not been used recently) should be recognized by the system. The causes for case deletion or addition might differ from other CBR applications: 1. Since images may be distorted and very noisy it might not be useful to store distorted representations. Determining which representation is distorted is sometimes not easy even if you have seen only a few images, and it is usually necessary to have domain knowledge that must also be built up over time. 2. Imprecise or noisy measurements can be caused by some defects of illumination, the image acquisition device, or the object itself.

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