Results 291 to 300 of about 11,467,392 (329)

Active Learning of Introductory Machine Learning

Proceedings. Frontiers in Education. 36th Annual Conference, 2006
This paper describes a computer-based training program for active learning of Agent Technology, Expert Systems, Neural Networks and Case-Based Reasoning by undergraduate students using a simple agent framework. While many Machine Learning (ML) and Artificial Intelligence (AI) courses teach ML and AI concepts by means of programming assignments, these ...
Maja Pantic, Reinier Zwitserloot
openaire   +1 more source

Comparative analysis of machine learning methods for active flow control

Journal of Fluid Mechanics, 2022
Machine learning frameworks such as genetic programming and reinforcement learning (RL) are gaining popularity in flow control. This work presents a comparative analysis of the two, benchmarking some of their most representative algorithms against global
F. Pino, Lorenzo Schena, J. Rabault, A. Kuhnle, M. A. Mendez   +4 more
semanticscholar   +1 more source

Active Learning for Neural Machine Translation

2018 International Conference on Asian Language Processing (IALP), 2018
Neural machine translation (NMT) normally requires a large bilingual corpus to train a high-translation-quality model. However, building such parallel corpora for many low-resource language pairs is rather expensive. In this paper, we propose to select informative source sentences to build a parallel corpus under the active learning framework so as to ...
Pei Zhang, Xueying Xu, Deyi Xiong
openaire   +2 more sources

Interactive Machine Learning for Data Exfiltration Detection: Active Learning with Human Expertise

IEEE International Conference on Systems, Man and Cybernetics, 2020
Data exfiltration is a serious threat to organizations. Such exfiltrations cause breach events that can lead to millions of dollars of loss. Perimeter defense is not enough by itself since successful exploits from insiders can also be very damaging ...
Mu-Huan Chung, M. Chignell, Lu Wang, Alexandra Jovicic, Abhay Raman   +4 more
semanticscholar   +1 more source

Autonomous Construction of Phase Diagrams of Block Copolymers by Theory-Assisted Active Machine Learning.

ACS Macro Letters, 2021
Equilibrium phase diagrams serve as blueprints for rational design of nanostructured materials of block copolymers, but their construction is time-consuming and requires profound expertise.
Shuochen Zhao, Tianyu Cai, Liang-Shun Zhang, Weihua Li, Jiaping Lin   +4 more
semanticscholar   +1 more source

Active Machine Learning for Consideration Heuristics

Marketing Science, 2011
We develop and test an active-machine-learning method to select questions adaptively when consumers use heuristic decision rules. The method tailors priors to each consumer based on a “configurator.” Subsequent questions maximize information about the decision heuristics (minimize expected posterior entropy).
Daria Dzyabura, John R. Hauser
openaire   +1 more source

Active Sampling for Learning Interpretable Surrogate Machine Learning Models

2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), 2020
The use of machine learning methods to inform consequential decisions is increasingly expanding across many fields. As a result, the ability to interpret these models has become to a greater extent crucial to increase the related-technologies acceptance level and reliability. In this paper, we propose an active sampling approach for learning accurately
Amal Saadallah, Katharina Morik
openaire   +2 more sources

The Science Behind Machine Learning, Deep Learning, and Active Learning

Dental Clinics of North America
This article introduces the core concepts of machine learning, deep learning (DL), and active learning (AL) and their impact on modern dentistry. It explains how these artificial intelligence technologies enable automated analysis of complex dental data, including the detection and segmentation of periapical lesions from cone-beam computed tomography ...
Rui Qi, Chen, Yeonju, Lee, Jing, Li
openaire   +2 more sources

The extreme learning machine learning algorithm with tunable activation function

Neural Computing and Applications, 2012
In this paper, we propose an extreme learning machine (ELM) with tunable activation function (TAF-ELM) learning algorithm, which determines its activation functions dynamically by means of the differential evolution algorithm based on the input data. The main objective is to overcome the problem dependence of fixed slop of the activation function in ...
Bin Li 0042, Yibin Li 0001, Xuewen Rong
openaire   +2 more sources

Activation-Kernel Extraction through Machine Learning

2017 New Generation of CAS (NGCAS), 2017
Machine Learning flooded many research fields, including Electronic Design Automation (EDA). The availability of algorithms that can solve complex problems through generic rule formulations represent a fresh opportunity to improve existing design paradigms. In this work we investigate the use of machine learning to manipulate logic circuits.
Valerio Tenace, Andrea Calimera
openaire   +1 more source