A recessive active learning method: enhancing the performance of predict models by adjusting the structure of data space

2021 ◽  
Author(s):  
Sun WeiYu ◽  
Chen Yin ◽  
Ji ChunYu
Keyword(s):  
Active Learning ◽  
Learning Method ◽  
Data Space ◽  
Active Learning Method

Quality-driven Deep Active Learning Method for 3D Brain MRI Segmentation

2021 ◽  
Author(s):  
Zhenxi Zhang ◽  
Jie Li ◽  
Chunna Tian ◽  
Zhusi Zhong ◽  
Zhicheng Jiao ◽  
...  
Keyword(s):  
Active Learning ◽  
Brain Mri ◽  
Learning Method ◽  
Mri Segmentation ◽  
Active Learning Method

scholarly journals THE EFFECT OF ACTIVE LEARNING METHOD ON STUDENTS’ ATTITUDE TOWARDS MATHEMATICS

2020 ◽  
Vol 9 ◽  
Author(s):  
Bibigul Kazmagambet ◽  
Zhansaya Ibraimova ◽  
Serkan Kaymak
Keyword(s):  
Active Learning ◽  
Learning Strategy ◽  
Teaching Strategy ◽  
Learning Method ◽  
Learning Group ◽  
Attitude Towards Mathematics ◽  
Active Learning Method ◽  
The Republic ◽  
Active Learning Strategy ◽  
The Impact

The world is changing so fast, and therefore education needs to adapt to the challenges of times. In order to update the content of school education in the Republic of Kazakhstan modern trends are going to be used. These trends contain pedagogical methods that can be used to preserve and even increase internal motivation, as active learning. Active learning method is an treatment where students participate or interact with the learning process, as opposed to passively taking in the information.The goal of this study is to identify the impact of active learning method on 10th grade students’ attitude towards mathematics of the students the second semester of the school year 2019-2020. More specifically, it attempted to determine and compare the attitude toward mathematics of students’ exposure to active learning and traditional teaching strategy. The Likert scale used to evaluate the attitude of students toward mathematics. Mean, Cronbach  value, T-test were the statistical tools used in anatomizing and interpreting the research data. The discovering showed that the students in the active learning group had auspicious attitude than students in the conventional teaching group. According to the findings after research, we saw the direct relation between attitude and active learning. It is concluded that the students’ attitude toward mathematics was better by using active learning strategy. It is recommended that mathematics teacher should use active learning strategy in order to improve the attitude toward mathematics of the students.Keywords:  attitude, mathematics, active learning

Beyond plagiarism: An active learning method to analyze causes behind code-similarity

2014 ◽  
Vol 70 ◽  
pp. 161-172 ◽  
Author(s):  
Feng-Pu Yang ◽  
Hewijin Christine Jiau ◽  
Kuo-Feng Ssu
Keyword(s):  
Active Learning ◽  
Learning Method ◽  
Active Learning Method

scholarly journals An Intelligent Multi-View Active Learning Method Based on a Double-Branch Network

Entropy ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. 901
Author(s):  
Fucong Liu ◽  
Tongzhou Zhang ◽  
Caixia Zheng ◽  
Yuanyuan Cheng ◽  
Xiaoli Liu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Active Learning ◽  
Sample Selection ◽  
Training Dataset ◽  
Learning Method ◽  
Active Learning Method ◽  
Level Information ◽  
The Stability ◽  
Different Characteristics

Artificial intelligence is one of the most popular topics in computer science. Convolutional neural network (CNN), which is an important artificial intelligence deep learning model, has been widely used in many fields. However, training a CNN requires a large amount of labeled data to achieve a good performance but labeling data is a time-consuming and laborious work. Since active learning can effectively reduce the labeling effort, we propose a new intelligent active learning method for deep learning, which is called multi-view active learning based on double-branch network (MALDB). Different from most existing active learning methods, our proposed MALDB first integrates two Bayesian convolutional neural networks (BCNNs) with different structures as two branches of a classifier to learn the effective features for each sample. Then, MALDB performs data analysis on unlabeled dataset and queries the useful unlabeled samples based on different characteristics of two branches to iteratively expand the training dataset and improve the performance of classifier. Finally, MALDB combines multiple level information from multiple hidden layers of BCNNs to further improve the stability of sample selection. The experiments are conducted on five extensively used datasets, Fashion-MNIST, Cifar-10, SVHN, Scene-15 and UIUC-Sports, the experimental results demonstrate the validity of our proposed MALDB.

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