ACTIVE learningCOGNITIVE scienceSAMPLING (Process)This paper studies a new problem, active learning with partial labels (ALPL). In this setting, an oracle annotates the query samples with partial labels, relaxing the oracle from the demanding accurate labeling process. To address ALPL, we first ...
3. Active Learning for Imbalanced Datasets 3.1. Problem Description We propose a formalization of AL in an imbalanced set- ting. We consider a source domain DS represented by DLS a labeled dataset with xi, yi ∈ X × YS for i = 1..nS, i.i.d realizations of random variables X , YS...
Selecting Influential Examples: Active Learning with Expected Model Output Changes Alexander Freytag , Erik Rodner , and Joachim Denzler Computer Vision Group, Friedrich Schiller University Jena, Germany {firstname.lastname}@uni-jena.de http://www.inf-cv.uni-jena.de Abstract. In this paper, we ...
An oracle (often a human expert) labels the selected instances. These are added to the training set, and the BDNN is retrained on the updated training set. This process is then repeated, with the training set increasing in size over time. b, Applications of DBAL. Embodied learning systems,...
Active learning is a type of machine learning where the model is trained on only the most relevant data. Explore the benefits and limitations of the framework.
However, other approaches to inference based on machine learning methods can also be used for this purpose. The concept of utility function was previously introduced in decision theory42 in the context of value of information (and hence uncertainties) and this provides us with the means to make ...
Based on this assumption, we propose a unified framework to actively construct entity-correspondence mappings across recommender systems, where a flexible transfer learning approach with partial entity-correspondence mappings between systems and a strategy for actively constructing cross-system correspondences ...
Previously, active learning has been so poorly defined that some have proposed we do away with the term entirely (Cooper 2016). However, we contend that active learning continues to be a useful construct. Prominent studies have greatly increased awareness of the term “active learning” among ...
Online active learning is a paradigm in machine learning that aims to select the most informative data points to label from a data stream. The problem of m
active learning. In recent research, Qian et al. (2013) introduced a pairwise query selection method under a layered hashing framework. A drawback of query level data sampling is that it may include some non-informative documents when there are a large number of documents associated with the ...