• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.3
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• pp.144-154
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• 2003

In this paper we present Logical Evolution method which is a new teaming algorithm for the concepts expressed as binary logic function. We try to solve some problems of Inductive Learning algorithms through Logical Evolution. First, to be less affected from limited prior knowledge, it generates features using the gained informations during learning process and learns the concepts with these features. Second, the teaming is done using not the whole example set but the individual example, so even if new problem or new input-output variables are given, it can use the previously generated features. In some cases these old features can make the teaming process more efficient. Logical Evolution method consists of 5 operations which are selected and performed by the logical evaluation procedure for feature generation and learning process. To evaluate the performance of the present algorithm, we make experiments on MONK data set and a newly defined problem.

• The Transactions of the Korea Information Processing Society
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• v.4 no.9
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• pp.2299-2308
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• 1997

While most machine learning research has been primarily concerned with the development of systems that implement one type of learning strategy, we use a multistrategy approach which integrates rule induction learning and instance-based learning, and show how this marriage allows for overall better performance. In the rule induction learning phase, we derive an entropy function, based on Hellinger divergence, which can measure the amount of information each inductive rule contains, and show how well the Hellinger divergence measures the importance of each rule. We also propose some heuristics to reduce the computational complexity by analyzing the characteristics of the Hellinger measure. In the instance-based learning phase, we improve the current instance-based learning method in a number of ways. The system has been implemented and tested on a number of well-known machine learning data sets. The performance of the system has been compared with that of other classification learning technique.

• Journal of The Korean Association For Science Education
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• v.23 no.2
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• pp.190-199
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• 2003

The purposes of this study was to investigate the effect of inductive Instructional method and deductive one concerning the learners' population genetic concepts and achievement according to learners' cognitive characteristics. For the study, 180 students were sampled from a boys' high school: 90 students for inductive teaching method and 90 students for deductive teaching method. Group Assessment of Logical Thinking(GALT) and Group Embedded Figure Test (GEFT) were used as the measure of cognitive characteristics. The results of this study were as follows. 1) The inductive instructional method was more effective in the understanding of population genetic concepts and their achievement. 2) Inductive instructional method was more effective than deductive one for the learners in formal operational level and in field independent cognitive style. 3) For the learners in a transitional level and field dependent cognitive style, deductive instructional way was more effective than inductive way on the average, but it was not statistically significant. It was turned out that learners' cognitive level was one of important factors when teachers instruct the concept of population genetics.

• The Mathematical Education
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• v.42 no.5
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• pp.647-672
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• 2003

This study sought to determine the impact of the graphing calculator on prospective math-teachers' mathematical thinking while they engaged in the exploratory tasks. To understand students' thinking processes, two groups of three students enrolled in the college of education program participated in the study and their performances were audio-taped and described in the observers' notebooks. The results indicated that the prospective teachers got the clues in recalling the prior memory, adapting the algebraic knowledge to given problems, and finding the patterns related to data, to solve the tasks based on inductive, deductive, and creative thinking. The graphing calculator amplified the speed and accuracy of problem-solving strategies and resulted partly in students' progress to the creative thinking by their concept development.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.1-8
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• 2019

Among the methods for extracting the most appropriate information from a large amount of log data, there is a method using inductive inference. In this paper, we use SVM (Support Vector Machine), which is an excellent classification method for inductive inference, in order to determine the ranking of intrusion logs in digital forensic analysis. For this purpose, the logs of the training log set are classified into intrusion logs and normal logs. The associated words are extracted from each classified set to generate a related word dictionary, and each log is expressed as a vector based on the generated dictionary. Next, the logs are learned using the SVM. We classify test logs into normal logs and intrusion logs by using the log set extracted through learning. Finally, the recommendation orders of intrusion logs are determined to recommend intrusion logs to the forensic analyst.

• Journal of Digital Convergence
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• v.17 no.10
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• pp.77-84
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• 2019

The purpose of this study is to examine about various barriers recognized by university faculty members while implementing PBL. By adopting the inductive content analysis, this study has analyzed the contents related with the barriers about PBL implementing in the teaching reflection reports submitted by 32 professors of B university. After the analysis, the barriers have been summarized into 5 major topics such as 'teaching beliefs,' 'classroom culture,' 'learning facilitation,' 'assessment,' 'school environment.' Results suggest: First, a study on the specific solutions for the barriers summarized by 5 major topics should be launched. Second, a teaching competency development program to resolve the barriers should be supported. Third, an innovation of physical school environment and school policy appropriate for PBL implementing should be involved. Fourth, a study on the barriers about PBL implementing should be further expanded.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.38 no.6
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• pp.18-25
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• 2001

KBANN (knowledge-based artificial neural network) combining the analytical learning and the inductive learning has been shown to be more effective than other machine learning models. However KBANN doesn't have the theory refinement ability because the topology of network can't be altered dynamically. Although TopGen was proposed to extend the ability of KABNN in this respect, it also had some defects. The algorithms which could solve this TopGen's defects, enabling the refinement of theory, by extending KBANN, are designed.

• Journal of Korean Home Economics Education Association
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• v.6 no.2
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• pp.161-174
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• 1994

In this study, among the cognitive learning models for information process, Taba’s inductive thinking model and Joyce&Weil’s concept attainment model, which help to obtain and study the concepts, can be applied to the ranges of consumer-education. Considering this, a new teaching-paln can be made. Applying the plan to the present teaching environments. I will do the research possibilities of applying the concept-learning teaching-plan to the consumer education. In the method of this research, many books, related to home economics & consumer-education, characters of concept-learning, and concept teaching-learning models, were studied. Also, on the basis of theoretical evidence, the teaching-plan, that apply the concept teaching model, were made. In addition, experimental research was done to find out the possibilities that the plan focusing on concept learning was applied or not. As a result of this study, two points are briefly summarized : 1. The teaching plan using Taba’s and Joyce & Weil’s concept-attainment model was made. 2. Concept-learning in consumer-education didn’t have a great a great influence to the changes of consumer-roles and attitudes, but had a great influence to the effects of consumer concept-knowledge(p<0.01) The effects of consumer-knowledge had much relation to consumer-roles and attitudes. The learners whose grade is higher in attainments of consumer-knowledge, also have a high grade in consumer-roles and attitudes.

• 한국경영정보학회:학술대회논문집
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• 2008.06a
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• pp.177-182
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• 2008

본 논문에서는 인터넷 비즈니스 모델의 성공과 실패 요인에 대하여 분석하고자 한다. 이를 위해서 유료화 관련 사례들을 분석하여 인터넷 비즈니스 모델의 유료화에 영향을 미치는 변수를 도출하고 이를 귀납 학습을 통하여 인터넷 비즈니스 모델의 변화(특히 유료화)에 관한 가설을 생성하였으며, 사업 전략 측면에서 그 의미를 해석하였다.

• Journal of Intelligence and Information Systems
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• v.1 no.1
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• pp.23-41
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• 1995

Business researchers have traditionally used statistical techniques for classification. In late 1980's, inductive learning started to be used for business classification. Recently, neural network began to be a, pp.ied for business classification. This study reviews the business classification studies, identifies a neural network a, pp.oach as the most powerful classification tool, and discusses the problems and issues in neural network a, pp.ications.