Probabilistic Learning by Uncertainty Sampling with Non-Binary Relevance

2000 ◽  
pp. 292-313 ◽  
Author(s):  
Gianni Amati ◽  
Fabio Crestani
Keyword(s):  
Probabilistic Learning ◽  
Uncertainty Sampling ◽  
Binary Relevance

scholarly journals How to measure uncertainty in uncertainty sampling for active learning

2021 ◽  
Author(s):  
Vu-Linh Nguyen ◽  
Mohammad Hossein Shaker ◽  
Eyke Hüllermeier
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Sampling Strategies ◽  
Total Uncertainty ◽  
Uncertainty Sampling ◽  
Different Types ◽  
Alternative Approaches ◽  
Active Learner ◽  
Probabilistic Nature ◽  
Different Sources

AbstractVarious strategies for active learning have been proposed in the machine learning literature. In uncertainty sampling, which is among the most popular approaches, the active learner sequentially queries the label of those instances for which its current prediction is maximally uncertain. The predictions as well as the measures used to quantify the degree of uncertainty, such as entropy, are traditionally of a probabilistic nature. Yet, alternative approaches to capturing uncertainty in machine learning, alongside with corresponding uncertainty measures, have been proposed in recent years. In particular, some of these measures seek to distinguish different sources and to separate different types of uncertainty, such as the reducible (epistemic) and the irreducible (aleatoric) part of the total uncertainty in a prediction. The goal of this paper is to elaborate on the usefulness of such measures for uncertainty sampling, and to compare their performance in active learning. To this end, we instantiate uncertainty sampling with different measures, analyze the properties of the sampling strategies thus obtained, and compare them in an experimental study.

Beyond binary relevance

2008 ◽  
Vol 42 (2) ◽  
pp. 53-58 ◽  
Author(s):  
Paul N. Bennett ◽  
Ben Carterette ◽  
Olivier Chapelle ◽  
Thorsten Joachims
Keyword(s):  
Binary Relevance

scholarly journals Learning tractable probabilistic models for moral responsibility and blame

2021 ◽  
Vol 35 (2) ◽  
pp. 621-659
Author(s):  
Lewis Hammond ◽  
Vaishak Belle
Keyword(s):  
Moral Responsibility ◽  
Probabilistic Models ◽  
Autonomous Systems ◽  
Cancer Staging ◽  
Data Driven ◽  
Probabilistic Learning ◽  
Learning Framework ◽  
Decision Points ◽  
Self Driving Cars ◽  
Trolley Problems

AbstractMoral responsibility is a major concern in autonomous systems, with applications ranging from self-driving cars to kidney exchanges. Although there have been recent attempts to formalise responsibility and blame, among similar notions, the problem of learning within these formalisms has been unaddressed. From the viewpoint of such systems, the urgent questions are: (a) How can models of moral scenarios and blameworthiness be extracted and learnt automatically from data? (b) How can judgements be computed effectively and efficiently, given the split-second decision points faced by some systems? By building on constrained tractable probabilistic learning, we propose and implement a hybrid (between data-driven and rule-based methods) learning framework for inducing models of such scenarios automatically from data and reasoning tractably from them. We report on experiments that compare our system with human judgement in three illustrative domains: lung cancer staging, teamwork management, and trolley problems.

scholarly journals Geometric Analysis of Uncertainty Sampling for Dense Neural Network Layer

2021 ◽  
pp. 1-1
Author(s):  
Aziz Kocanaogullari ◽  
Niklas Smedemark-Margulies ◽  
Murat Akcakaya ◽  
Deniz Erdogmus
Keyword(s):  
Neural Network ◽  
Geometric Analysis ◽  
Network Layer ◽  
Uncertainty Sampling

Probabilistic Learning in Immune Network: Weighted Tree Matching Model

1996 ◽  
Vol 3 (1) ◽  
pp. 143-162 ◽  
Author(s):  
RAJANI R. JOSHI ◽  
K. KRISHNANAND
Keyword(s):  
Immune Network ◽  
Probabilistic Learning ◽  
Weighted Tree ◽  
Matching Model
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