Titlebook: Discovery Science; 15th International C Jean-Gabriel Ganascia,Philippe Lenca,Jean-Marc Pet Conference proceedings 2012 Springer-Verlag Berl

GLUE

名次后綴

custody

HCAC: Semi-supervised Hierarchical Clustering Using Confidence-Based Active Learningmi-supervised hierarchical clustering by using an active learning solution with cluster-level constraints. This active learning approach is based on a new concept of merge confidence in agglomerative clustering. When there is low confidence in a cluster merge the user is queried and provides a clust

NIB

LF-CARS: A Loose Fragment-Based Consensus Clustering Algorithm with a Robust Similaritying result from multiple data sources or to improve the robustness of clustering result. In this paper, we propose a novel definition of the similarity between points and clusters to represent how a point should join or leave a cluster clearly. With this definition of similarity, we desigh an iterat

Radiation

Climate

Online Co-regularized Algorithmsediction functions on unlabeled data points, our algorithm provides improved performance in comparison to supervised methods on several UCI benchmarks and a real world natural language processing dataset. The presented algorithm is particularly applicable to learning tasks where large amounts of (un

SPALL

Fast Progressive Training of Mixture Models for Model Selectionaging, and handling missing data. One of the prerequisites of using mixture models is the a priori knowledge of the number of mixture components so that the Expectation Maximization (EM) algorithm can learn the maximum likelihood parameters of the mixture model. However, the number of mixing compone

Androgen

游行

參考書目

Thomas Zumbroich,Andreas Müllere learning or data mining techniques. This is because machine learning and data mining have focussed on developing high-performance algorithms for solving particular tasks rather than on developing general principles and techniques. I propose to alleviate these problems by applying the constraint pr