Boullart. Kernel-based learning methods for preference aggregation. 4OR , 7:169–189, 2009.Waegeman, W., De Baets, B., Boullart, L.: Kernel-based learning methods for preference aggregation. 4OR: A Quarterly Jo
Kernel-based learning methods The kernel function—a function returning the inner product between mapped data points in a higher dimensional space—is a foundational building block for kernel-based learning methods. Such learning takes place in the feature space so long as the learning algorithm can ...
Xiong, F., Gou, M., Camps, O., Sznaier, M. (2014). Person Re-Identification Using Kernel-Based Metric Learning Methods. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds) Computer Vision – ECCV 2014. ECCV 2014. Lecture Notes in Computer Science, vol 8695. Spring...
Adaptive Kernel Based Machine Learning Methods.Adaptive Kernel Based Machine Learning Methods.Learning machinesAlgorithmsComputerized tomographyLearningNumerical analysisKernelResearch results obtained from this project address the kernel selection problem in machine learning. Specifically, motivated from the need of...
Kernel-basedLearningMethods byNelloCristianiniandJohn Shawe-Taylor ISBN:0521780195 CambridgeUniversityPress?2000(190pages) ThisisthefirstcomprehensiveintroductiontoSVMs,a newgenerationlearningsystembasedonrecent advancesinstatisticallearningtheory;itwillhelp
and Other Kernel-Based Learning Methods, Nello Cristianini and John Shawe-Taylor, Cambridge University Press, Cambridge, U.K., 2000, 189 pp., ISBN 0-521-78019-5. n eed t o in clude n on lin ear feat ure compon en t s. A useful t ech n ique for ...
An introduction to SVMs and other kernel-based learning methods (2000) Q. Dai et al. Improved CBP neural network model with applications in time series prediction Neural Process. Lett. (2003) R. Dutta et al. Bacteria classification using cyranose 320 electronic nose Biomed. Eng. Online (2002...
An Introduction to Kernel-Based Learning Algorithms.Provides an introduction to support vector machine (SVM), kernel Fisher discriminant (KFD) analysis and principal component analysis as examples for kernel-based learning methods. Basic concepts of learning theory; Nonlinear algorithms in kernel-feature ...
Kernel Methods (KMs) are powerful machine learning techniques that can alleviate the data representation problem as they substitute scalar product between feature vectors with similarity functions (kernels) directly defined between data instances, e.g., syntactic trees, (thus features are not needed any...
et al. Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning. Nat Methods 14, 414–416 (2017). https://doi.org/10.1038/nmeth.4207 Download citation Received09 June 2016 Accepted02 February 2017 Published06 March 2017 Issue DateApril 2017 DOIhttps://doi....