Kernel Methods and Machine Learning
By Sun-Yuan Kung. 2014
239 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Statistics and Probability
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Citations in the corpus
- Simulation-assisted machine learning (2018, background): “tion and machine learning steps also points out their individual contributions. The simulation-based kernel structures the space in which the samples live (or more technically, the dual of the space (Kung, 2014)), and ML nds the patterns in this simplied space. We see that in order to improve machin”
- Debugging Machine Learning Models (2016, background): “Recalling that we note that ˆ A is the Mahalanobis inner product (Kung, 2014) between the test data and training data.”
- A Hebbian/Anti-Hebbian Neural Network for Linear Subspace Learning: A Derivation from Multidimensional Scaling of Streaming Data (2015, methodology): “Our network is similar to the APEX network (Kung and Diamantaras, 1990) in the 7 As pointed before (Linsker, 1988; Plumbley, 1993, 1995; Kung, 2014), PCA maximizes mutual information between a Gaussian input, x , and an output, y = Fx , such that rows of F have unit norms.”