Human Active Learning

Abstract

Active machine learning (AML) is a popular research area in machine learning. It allows selection of the most informative instances in training data of the domain for manual labeling. AML aims to produce a highly accurate classifier using as few labeled instances as possible, thereby minimizing the cost of obtaining labeled data. As machines can learn from experience like humans do, using AML for human category learning may help human learning become more efficient and hence reduce the cost of teaching. This chapter is a review of recent research literature concerning the use of AML technique to enhance human learning and teaching. There are a few studies on the applications of AML to the human category learning domain. The most interesting study was by Castro et al., which showed that humans learn faster with better performance when they can actively select the informative instances from a pool of unlabeled data instead of random sampling. Although AML can facilitate object categorization for humans, there are still many challenges and questions that need to be addressed in the use of AML for modeling human categorization. In this chapter, we will discuss some of these challenges. Figure 5. A sample of images from the dataset used for teaching and test. Columns L1-L4 show different difficulty levels.Active Learning -Beyond the Future 26

Evaluating and Exploring the Effectiveness of Journalism and Communication Discipline Construction in the Context of Smart Era

“…In the era of intelligence, which is deeply rooted in the hearts of the people, the educational concept, model, and governance will be reshaped. e discipline construction of Johnson & Johnson under the background of the wisdom era is a reform project for the reconstruction of the discipline education of [13][14][15][16].…”

Construction of English Intelligent Translation Software Framework Based on Data Analysis Algorithm

“…(2) According to the distance density function ( 6), the neighborhood radius calculation formula (7), and the point density calculation formula ( 5), obtain the point density D(Xi) corresponding to each data object.…”

“…(3) According to the mean density calculation formula (7), the mean density of the dataset S is calculated as MD(x). (4) Calculate the point density of each data object obtained and then compare, and divide all data objects not less than the average density into a set M [14].…”

“…e network multilingual timely translation systems in [4,7] were introduced, and the test parameters were set, as shown in Table 1.…”

“…erefore, in this work, the English intelligent translation software based on data analysis algorithm is designed to provide inspiration and suggestions for college English education [7].…”