Support Vector Machines
PAC-Bayes bounds for stable algorithms with instance-dependent priors
Omar Rivasplata, Csaba Szepesvari, John S. Shawe-Taylor, Emilio Parrado-Hernandez, Shiliang Sun
Neural Information Processing Systems http://nips.cc/
Bayesian multi-domain learning for cancer subtype discovery from next-generation sequencing count data
Ehsan Hajiramezanali, Siamak Zamani Dadaneh, Alireza Karbalayghareh, Mingyuan Zhou, Xiaoning Qian
InMTL, there areDdifferent labeled domains where data are related and the goal is to improve the predictive power of all domains altogether.
Onthe Equivalencebetween Neural Networkand Support Vector Machine
Suppose l is -lipschitzand l-smoothforthefirstargument (i.e.
Graph Convolutional Kernel Machine versus Graph Convolutional Networks
An example is the graph convolutional kernel support vector machine (GCKSVM) for node classification, for which we analyze the generalization error bound and discuss the impact of the graph structure.
ReHLine: Regularized Composite ReLU-ReHU Loss Minimization with Linear Computation and Linear Convergence Ben Dai
Empirical risk minimization (ERM) is a crucial framework that offers a general approach to handling a broad range of machine learning tasks.
Reproducibility in Multiple Instance Learning: A Case For Algorithmic Unit Tests
They are able to learn anti-correlated instances, i.e., defaulting to "positive" labels until seeing a negative counter-example, which should not be possible for a correct MIL model.
8989e07fc124e7a9bcbdebcc8ace2bc0-Paper.pdf
Meta-learning improvesgeneralization ofmachine learning models when faced with previously unseen tasks by leveraging experiences from different, yet related prior tasks.