Titlebook: Similarity-Based Pattern Recognition; First International Marcello Pelillo,Edwin R. Hancock Conference proceedings 2011 Springer-Verlag Gm

减至最低

Section-Wise Similarities for Clustering and Outlier Detection of Subjective Sequential Datauential data is proposed. The results of the experiments show that both similarities contain complementary and relevant information about the dataset. The methodology results useful to find patterns on subjective data related with the behavior and the level of the data.

公猪

Multi-task Regularization of Generative Similarity Modelst groups in Iraq. We show how to produce the necessary task relatedness information from standard given training data, as well as how to derive task-relatedness information if given side information about the class relatedness.

RENAL

Multiple-Instance Learning with Instance Selection via Dominant Setsags, which is based on an effective pairwise clustering algorithm referred to as dominant sets. Experimental results on both standard benchmark data sets and on multi-class image classification problems show that the proposed approach is not only highly competitive with state-of-the-art MIL algorithms but also very robust to outliers and noise.

惰性气体

Bag Dissimilarities for Multiple Instance Learningstandard multiple instance classifiers. In particular a measure that computes just the average minimum distance between instances, or a measure that uses the Earth Mover’s distance, perform very well.

漂白

Combining Data Sources Nonlinearly for Cell Nucleus Classification of Renal Cell Carcinomature representation separately and three linear MKL algorithms from the literature. We demonstrate that our variant obtains more accurate classifiers than competing algorithms for RCC detection by combining information from different feature representations nonlinearly.

盟军

不可侵犯

One Shot Similarity Metric Learning for Action Recognitionimproved OSS performance. We test the proposed technique using the recently presented ASLAN action similarity labeling benchmark. Enhanced, state of the art performance is obtained, and the method compares favorably to leading similarity learning techniques.

interference

On a Non-monotonicity Effect of Similarity Measuresles. As an alternative approach Weyl’s discrepancy measure is examined by which this non-monotonicity effect can be avoided even for patterns with high-frequency or chaotic characteristics. The impact of the non-monotonicity effect to applications is discussed by means of examples from the field of stereo matching, texture analysis and tracking.

辞职

Impact of the Initialization in Tree-Based Fast Similarity Search Techniqueshowing that an adequate choice of this pivot leads to significant reductions in distance computations and time complexity..Moreover, most pivot tree-based indexes emphasizes in building balanced trees. We provide experimentally and theoretical support that very unbalanced trees can be a better choice than balanced ones.

reaching

0302-9743 and analysis; generative models of similarity data; graph-based and relational models; clustering and dissimilarity data; applications; spectral methods and embedding.978-3-642-24470-4978-3-642-24471-1Series ISSN 0302-9743 Series E-ISSN 1611-3349