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» On learning with dissimilarity functions
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KDD
2008
ACM
193views Data Mining» more  KDD 2008»
14 years 8 months ago
A family of dissimilarity measures between nodes generalizing both the shortest-path and the commute-time distances
This work introduces a new family of link-based dissimilarity measures between nodes of a weighted directed graph. This measure, called the randomized shortest-path (RSP) dissimil...
Luh Yen, Marco Saerens, Amin Mantrach, Masashi Shi...
ANNPR
2008
Springer
13 years 10 months ago
Patch Relational Neural Gas - Clustering of Huge Dissimilarity Datasets
Clustering constitutes an ubiquitous problem when dealing with huge data sets for data compression, visualization, or preprocessing. Prototype-based neural methods such as neural g...
Alexander Hasenfuss, Barbara Hammer, Fabrice Rossi
CVPR
2007
IEEE
14 years 10 months ago
Robust Click-Point Linking: Matching Visually Dissimilar Local Regions
This paper presents robust click-point linking: a novel localized registration framework that allows users to interactively prescribe where the accuracy has to be high. By emphasi...
Kazunori Okada, Xiaolei Huang
CORR
2008
Springer
91views Education» more  CORR 2008»
13 years 8 months ago
Natural pseudo-distance and optimal matching between reduced size functions
We study a dissimilarity measure between shapes, expressed by the natural pseudodistance between size pairs, where a shape is viewed as a topological space endowed with a real-val...
Michele d'Amico, Patrizio Frosini, Claudia Landi
ICMLA
2008
13 years 9 months ago
A Clustering Approach in Developing Prognostic Systems of Cancer Patients
Accurate prediction of survival rates of cancer patients is often key to stratify patients for prognosis and treatment. Survival prediction is often accomplished by the TNM system...
Dechang Chen, Kai Xing, Donald Henson, Li Sheng, A...