A kriging-based active learning algorithm for contour estimation of integrated response with noise factors

2021 ◽  
Author(s):  
Mei Han ◽  
Qianqian Huang ◽  
Linhan Ouyang ◽  
Xufeng Zhao
Keyword(s):  
Active Learning ◽  
Learning Algorithm ◽  
Contour Estimation ◽  
Integrated Response ◽  
Noise Factors

Adapting SVM for data sparseness and imbalance: a case study in information extraction

2009 ◽  
Vol 15 (2) ◽  
pp. 241-271 ◽  
Author(s):  
YAOYONG LI ◽  
KALINA BONTCHEVA ◽  
HAMISH CUNNINGHAM
Keyword(s):  
Active Learning ◽  
Language Learning ◽  
Information Extraction ◽  
Language Processing ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Support Vector ◽  
Passive Learning ◽  
Wide Range

AbstractSupport Vector Machines (SVM) have been used successfully in many Natural Language Processing (NLP) tasks. The novel contribution of this paper is in investigating two techniques for making SVM more suitable for language learning tasks. Firstly, we propose an SVM with uneven margins (SVMUM) model to deal with the problem of imbalanced training data. Secondly, SVM active learning is employed in order to alleviate the difficulty in obtaining labelled training data. The algorithms are presented and evaluated on several Information Extraction (IE) tasks, where they achieved better performance than the standard SVM and the SVM with passive learning, respectively. Moreover, by combining SVMUM with the active learning algorithm, we achieve the best reported results on the seminars and jobs corpora, which are benchmark data sets used for evaluation and comparison of machine learning algorithms for IE. In addition, we also evaluate the token based classification framework for IE with three different entity tagging schemes. In comparison to previous methods dealing with the same problems, our methods are both effective and efficient, which are valuable features for real-world applications. Due to the similarity in the formulation of the learning problem for IE and for other NLP tasks, the two techniques are likely to be beneficial in a wide range of applications1.

An active learning algorithm for multi-class classification

2018 ◽  
Vol 22 (3) ◽  
pp. 1051-1063
Author(s):  
Dongjiang Liu ◽  
Yanbi Liu
Keyword(s):  
Active Learning ◽  
Learning Algorithm ◽  
Multi Class Classification

Multiclass SVM active learning algorithm based on decision directed acyclic graph and one versus one

2018 ◽  
Vol 22 (S3) ◽  
pp. 6241-6251 ◽  
Author(s):  
Hailong Xu ◽  
Xiaofeng Bie ◽  
Hui Feng ◽  
Ye Tian
Keyword(s):  
Active Learning ◽  
Directed Acyclic Graph ◽  
Learning Algorithm ◽  
Acyclic Graph ◽  
Multiclass Svm

scholarly journals Active Learning Plus Deep Learning Can Establish Cost-Effective and Robust Model for Multichannel Image: A Case on Hyperspectral Image Classification

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4975
Author(s):  
Fangyu Shi ◽  
Zhaodi Wang ◽  
Menghan Hu ◽  
Guangtao Zhai
Keyword(s):  
Deep Learning ◽  
Active Learning ◽  
Image Classification ◽  
Large Scale ◽  
Hyperspectral Image ◽  
Image Annotation ◽  
Learning Algorithm ◽  
Magnetic Resonance Images ◽  
Biological Engineering ◽  
Hyperspectral Image Classification

Relying on large scale labeled datasets, deep learning has achieved good performance in image classification tasks. In agricultural and biological engineering, image annotation is time-consuming and expensive. It also requires annotators to have technical skills in specific areas. Obtaining the ground truth is difficult because natural images are expensive. In addition, images in these areas are usually stored as multichannel images, such as computed tomography (CT) images, magnetic resonance images (MRI), and hyperspectral images (HSI). In this paper, we present a framework using active learning and deep learning for multichannel image classification. We use three active learning algorithms, including least confidence, margin sampling, and entropy, as the selection criteria. Based on this framework, we further introduce an “image pool” to make full advantage of images generated by data augmentation. To prove the availability of the proposed framework, we present a case study on agricultural hyperspectral image classification. The results show that the proposed framework achieves better performance compared with the deep learning model. Manual annotation of all the training sets achieves an encouraging accuracy. In comparison, using active learning algorithm of entropy and image pool achieves a similar accuracy with only part of the whole training set manually annotated. In practical application, the proposed framework can remarkably reduce labeling effort during the model development and upadting processes, and can be applied to multichannel image classification in agricultural and biological engineering.

scholarly journals Bias Free Multiobjective Active Learning for Materials Design and Discovery

2020 ◽  
Author(s):  
Kevin Maik Jablonka ◽  
Giriprasad Melpatti Jothiappan ◽  
Shefang Wang ◽  
Berend Smit ◽  
Brian Yoo
Keyword(s):  
Active Learning ◽  
De Novo ◽  
Learning Algorithm ◽  
Random Search ◽  
Search Space ◽  
Machine Learning Techniques ◽  
Pareto Optimal ◽  
Design Rules ◽  
Learning Techniques ◽  
Screening Approaches

<div>The design rules for materials are clear for applications with a single objective. For most applications, however, there are often multiple, sometimes competing objectives where there is no single best material, and the design rules change to finding the set of Pareto optimal materials. </div><div>In this work, we introduce an active learning algorithm that directly uses the Pareto dominance relation to compute the set of Pareto optimal materials with desirable accuracy. <br></div><div>We apply our algorithm to de novo polymer design with a prohibitively large search space.</div><div>Using molecular simulations, we compute key descriptors for dispersant applications and reduce the number of materials that need to be evaluated to reconstruct the Pareto front with a desired confidence by over 98% compared to random search.</div><div>This work showcases how simulation and machine learning techniques can be coupled to discover materials within a design space that would be intractable using conventional screening approaches.</div>

scholarly journals Bias Free Multiobjective Active Learning for Materials Design and Discovery

2020 ◽  
Author(s):  
Kevin Maik Jablonka ◽  
Giriprasad Melpatti Jothiappan ◽  
Shefang Wang ◽  
Berend Smit ◽  
Brian Yoo
Keyword(s):  
Active Learning ◽  
De Novo ◽  
Learning Algorithm ◽  
Random Search ◽  
Search Space ◽  
Machine Learning Techniques ◽  
Pareto Optimal ◽  
Design Rules ◽  
Learning Techniques ◽  
Screening Approaches

<div>The design rules for materials are clear for applications with a single objective. For most applications, however, there are often multiple, sometimes competing objectives where there is no single best material, and the design rules change to finding the set of Pareto optimal materials. </div><div>In this work, we introduce an active learning algorithm that directly uses the Pareto dominance relation to compute the set of Pareto optimal materials with desirable accuracy. <br></div><div>We apply our algorithm to de novo polymer design with a prohibitively large search space.</div><div>Using molecular simulations, we compute key descriptors for dispersant applications and reduce the number of materials that need to be evaluated to reconstruct the Pareto front with a desired confidence by over 98% compared to random search.</div><div>This work showcases how simulation and machine learning techniques can be coupled to discover materials within a design space that would be intractable using conventional screening approaches.</div>

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