ﻻ يوجد ملخص باللغة العربية
The C-bound, introduced in Lacasse et al., gives a tight upper bound on the risk of a binary majority vote classifier. In this work, we present a first step towards extending this work to more complex outputs, by providing generalizations of the C-bound to the multiclass and multi-label settings.
We study an extreme scenario in multi-label learning where each training instance is endowed with a single one-bit label out of multiple labels. We formulate this problem as a non-trivial special case of one-bit rank-one matrix sensing and develop an
Multi-label text classification is a popular machine learning task where each document is assigned with multiple relevant labels. This task is challenging due to high dimensional features and correlated labels. Multi-label text classifiers need to be
This paper generalizes an important result from the PAC-Bayesian literature for binary classification to the case of ensemble methods for structured outputs. We prove a generic version of the Cbound, an upper bound over the risk of models expressed a
Many Machine Learning algorithms, such as deep neural networks, have long been criticized for being black-boxes-a kind of models unable to provide how it arrive at a decision without further efforts to interpret. This problem has raised concerns on m
We consider a problem of multiclass classification, where the training sample $S_n = {(X_i, Y_i)}_{i=1}^n$ is generated from the model $mathbb P(Y = m | X = x) = eta_m(x)$, $1 leq m leq M$, and $eta_1(x), dots, eta_M(x)$ are unknown $alpha$-Holder co