Aug 26, 2019 · Unlike traditional supervised learning that relies on fixed models, NEL utilizes self-adjusting machine learning to better accommodate the non- ...

Aug 26, 2019 · In this paper, we introduce a new NEL algorithm that builds on a highly flexible I2GMM data model. [13] and aims to unify all learning tasks by ...

Non-exhaustive learning (NEL) is an emerging machine-learning paradigm designed to confront the challenge of non-stationary environments characterized by ...

A Nonparametric Bayesian Perspective for Machine Learning in Partially-Observed Settings ... Bayesian Nonparametrics for Non-exhaustive Learning · Yicheng Cheng ...

Nonexhaustive learning when implemented in this domain in an online fashion aims at rapid identification of new, emerging classes of mi- croorganisms, which are ...

For Bayesian nonparametrics, the non-random component is not of interest, so we always assume ξn = 0. In the prior, we usually have no reason to assume ...

Missing: exhaustive | Show results with:exhaustive

Skills or low-level policies in reinforcement learn- ing are temporally extended actions that can speed up learning and enable complex behaviours.

This paper provides an overview of non- parametric Bayesian models relevant to natural language processing (NLP) tasks. We first introduce Bayesian paramet-.

Missing: Non- exhaustive