Multiple kernel learningMetric learningGating functionKernel weightDistance metric learning aims to learn a data-dependent similarity measure, which is widely employed in machine learning. Recently, metric learning algorithms that incorporate multiple kernel learning have shown promising outcomes for ...
The success of kernel methods is very much dependent on the choice of kernels. Multiple kernel learning (MKL) aims at learning a combination of different k... Tinghua Wang a b,Dongyan Zhao b,Yansong Feng b - Knowledge-Based Systems 被引量: 36发表: 2013年 A kernel learning framework for...
Selection of K is user dependent; it is an odd number. Mostly, the value of K is square root of n. Distance metrics used in KNN are Minkowski, Manhattan, and Euclidean distances. Minkowski distance equation is generalized, and we can derive Manhattan and Euclidean distances from it. (15)...
Distance metric learning is a fundamental problem in data mining and knowledge discovery. Many representative data mining algorithms, such as k-nearest neighbor classifier, hierarchical clustering and spectral clustering, heavily rely on the underlying distance metric for correctly measuring relations among ...
Bandyopadhyay and Pal [12] propose new distance measures based on Euclidean and Manhattan distance measures where normalization is dependent on the experiment type, i.e., samples. Balasubramaniyan et al. [9] also use a local, shape-based distance metric based on the Spearman rank correlation. ...
Smooth orientation-dependent scoring function for coarse-grained protein qual- ity assessment. Bioinformatics. 2019;35(16):2801–8. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ...
RSD: A Metric for Achieving Range-Free Localization beyond Connectivity Wirelessensor networksaveeenonsidereds promisingoolorany location-dependentpplications. Inuch deployments,heequirementf lowystemost prohibitsanyange-basede... Zhong,Ziguo,Tian - 《IEEE Transactions on Parallel & Distributed Systems...
have been too coarsely chosen to detect the resulting effects. To address each of these three limitations, we conducted Experiment 2 in which we replicated study one within a larger sample and relying on both a more fine-grained manipulation of goal similarity and a more sensitive dependent ...
@inproceedings{fad_embeddings, title={Correlation of Fr{\'e}chet Audio Distance With Human Perception of Environmental Audio Is Embedding Dependent}, author={Tailleur, Modan and Lee, Junwon and Lagrange, Mathieu and Choi, Keunwoo and Heller, Laurie M and Imoto, Keisuke and Okamoto, Yuki}, ...
The choice of a distance or similarity metric is, however, not trivial and can be highly dependent on the application at hand. In this work, we propose a novel metric learning method to evaluate distance between graphs that leverages the power of convolutional neural networks, while exploiting ...