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Found 5 papers, 0 papers with code
Separability and Scatteredness (S&S) Ratio-Based Efficient SVM Regularization Parameter, Kernel, and Kernel Parameter Selection
By stochastically analyzing the behavior of the regularization parameter, this work shows that the SVM performance can be modeled as a function of separability and scatteredness (S&S) of the data.
Learnability, Sample Complexity, and Hypothesis Class Complexity for Regression Models
The proposed approach, denoted by epsilon-Confidence Approximately Correct (epsilon CoAC), utilizes Kullback Leibler divergence (relative entropy) and proposes a new related typical set in the set of hyperparameters to tackle the learnability issue.
Hyperspectral Image Classification Based on Sparse Modeling of Spectral Blocks
In this paper, a sparse modeling framework is proposed for hyperspectral image classification.
Efficient Low Dose X-ray CT Reconstruction through Sparsity-Based MAP Modeling
In the proposed method we use pseudo polar Fourier transform as the measurement matrix in order to decrease the computational complexity; and rebinning of the projections to parallel rays in order to extend its application to fan beam and helical cone beam scans.
Efficient unimodality test in clustering by signature testing
This paper provides a new unimodality test with application in hierarchical clustering methods.
