نتایج جستجو برای: unsupervised active learning method
تعداد نتایج: 2505811 فیلتر نتایج به سال:
abstract this study is an attempt to determine the effect of nano- technology education on science lesson for fifth grade in primary school in the city of kermanshah. this research is experimental and is conducted in the from of pre-test and post- test for the control group. an achievement test was administered to determine the rate of learning in the students. the research is an applied one. ...
Unsupervised learning and supervised remote teleoperator control for robots may seem an unlikely combination. This paper argues that the combination holds advantages for both parties. The operator would like to “instruct” the robot without any special effort, and then be able to hand over some or all of the tasks to be performed without loss of overall supervisory control. In return, the learni...
Classification methods can be divided into supervised and unsupervised methods. The supervised classifier requires a training set for the classifier parameter estimation. In the case of absence of a training set, the popular classifiers (e.g. K-Nearest Neighbors) can not be used. The clustering methods are considered as unsupervised classification methods. This paper presents an idea of the uns...
Feature selection is effective in removing irrelevant data. However, the result of feature selection in unsupervised learning is not as satisfying as that in supervised learning. In this paper, we propose a novel methodology ULAC (Feature Selection for Unsupervised Learning Based on Attribute Correlation Analysis and Clustering Algorithm) to identify important features for unsupervised learning...
Introduction: Active Learning Method (ALM) is a model in which students are active in the class. This aim of this study is to compare stability of information and satisfaction of students in classic method of lecture and active learning method. Methods: This descriptive cross-sectional study was performed on 48 medical students (29 females and 19 males) selected through census sampling method ...
We consider learning a sequence classifier without labeled data by using sequential output statistics. The problem is highly valuable since obtaining labels in training data is often costly, while the sequential output statistics (e.g., language models) could be obtained independently of input data and thus with low or no cost. To address the problem, we propose an unsupervised learning cost fu...
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