• Korean Journal of Logic
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• v.12 no.2
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• pp.59-88
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• 2009

According to David Hume, a deductive demonstration for inductive inference is not possible, because inductive inference is not deductive; and an inductive demonstration for inductive inference is not possible either, because such a demonstration is circular. Thus, on his view, there is no way of justifying inductive inference. Ever since Hume raised this problem of induction, a fair number of philosophers have tried to solve it. Nevertheless there is still no solution which is plausible enough to receive wide endorsement. According to Wilfrid Sellars, we cannot justify inductive inference by any theoretical reasoning; we can vindicate it only by a certain sort of practical reasoning. In this paper, I defend this Sellarsian proposal by developing and explaining it.

• Child Studies in Asia-Pacific Contexts
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• v.5 no.2
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• pp.83-94
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• 2015

The purpose of this study was to understand the influence of personality trait information on young children's perception of initial attraction in peer relationships. The sample consisted of 90 children of three to five years of age in South Korea. Children were presented with an inductive inference task where they had to make inference of a target character's preference on novel-play and prosocial act based on trait labels (smart-not smart, outgoing-shy, nice-mean) and perceptual (toy) similarity information of two test characters. Children showed difference in their use of trait information depending on the perceptual similarity information, trait valence, and inference question with age. This result provides initial support that not only do young children understand the significance of trait in peer attraction but also know when trait label is more informative to use to infer others depending on the situation.

• Journal of KIISE:Software and Applications
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• v.37 no.9
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• pp.659-668
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• 2010

Collaborative filtering systems grasp behaviors for a new user and need new information for the user in order to recommend interesting items to the user. For the purpose of acquiring the information the collaborative filtering systems learn behaviors for users based on the previous data and can obtain new information from the results. In this paper, we propose an inductive inference method to obtain new information for users and rank items by using the new information in the proposed method. The proposed method clusters users into groups by learning users through NMF among inductive machine learning methods and selects the group features from the groups by using chi-square. Then, the method classifies a new user into a group by using the bayesian probability model as one of inductive inference methods based on the rating values for the new user and the features of groups. Finally, the method decides the ranks of items by applying the Rocchio algorithm to items with the missing values.

• 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.

• Korean Journal of Logic
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• v.16 no.1
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• pp.87-115
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• 2013

In my previous article "The Uncontested Principle and Wonbae Choi's Objections", I argued that the validity of modus ponens (as a deductive inference) is compatible with the claim that the Uncontested Principle is controversial. In his recent paper "The Uncontested Principle and Modus Ponens", Wonbae Choi criticizes my view again by making the following three claims: First, even though I do not take an inference of the form 'If A then (probably) C. A. $\therefore$ C' as an instance of modus ponens, this form of inference can be taken to be such an instance. Second, there is no grammatical indicator which allows us to distinguish between an indicative conditional based on a deductive inference and an indicative conditional based on an inductive inference, so that inferences based on these conditionals should not be treated as different types of inferences. Third, if we allow an indicative conditional based on an inductive inference, we thereby violate the so-called 'principle of harmony', which any logical concept should preserve. In this paper, I reply that his criticisms are all implausible.

• Journal of Practical Engineering Education
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• v.6 no.1
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• pp.1-7
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• 2014

The variable control in the inductive inference for the confirmation and verification when the experimental data are collected is studied by applying the principle of probability inference. The control in engineering experiments is to protect any effect by of intervening variable except primary independent variable on the dependent variable. By the special condition the possibility for developing a phenomenon will be maximized; otherwise, by the extraneous condition the possibility for developing a phenomenon will be minimized. By doing so, the control may provide insurance for the causal relationship between the certain prior event (independent variable) and the post-event (the dependent variable). Some experiments by using both elliptical trainer and tread mill under the variable control are performed in order to find the relations between the energy expenditure, the respiratory exchange ratio (RER), and the heart rate (HR) against the exercise speed.

• School Mathematics
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• v.14 no.1
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• pp.85-107
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• 2012

Problem solving is important in school mathematics as the means and end of mathematics education. In elementary school, inductive reasoning is closely linked to problem solving. The purpose of this study was to examine ways of improving problem solving ability through analysis of inductive reasoning process. After the process of inductive reasoning in problem solving was analyzed, five different stages of inductive reasoning were selected. It's assumed that the flow of inductive reasoning would begin with stage 0 and then go on to the higher stages step by step, and diverse sorts of additional inductive reasoning flow were selected depending on what students would do in case of finding counter examples to a regulation found by them or to their inference. And then a case study was implemented after four elementary school students who were in their sixth grade were selected in order to check the appropriateness of the stages and flows of inductive reasoning selected in this study, and how to teach inductive reasoning and what to teach to improve problem solving ability in terms of questioning and advising, the creation of student-centered class culture and representation were discussed to map out lesson plans. The conclusion of the study and the implications of the conclusion were as follows: First, a change of teacher roles is required in problem-solving education. Teachers should provide students with a wide variety of problem-solving strategies, serve as facilitators of their thinking and give many chances for them ide splore the given problems on their own. And they should be careful entegieto take considerations on the level of each student's understanding, the changes of their thinking during problem-solving process and their response. Second, elementary schools also should provide more intensive education on justification, and one of the best teaching methods will be by taking generic examples. Third, a student-centered classroom should be created to further the class participation of students and encourage them to explore without any restrictions. Fourth, inductive reasoning should be viewed as a crucial means to boost mathematical creativity.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.296-301
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• 1998

The Rough Set theory suggested by Pawlak in 1982 has been useful in AI, machine learning, knowledge acquisition, knowledge discovery from databases, expert system, inductive reasoning. etc. The main advantages of rough set are that it does not need any preliminary or additional information about data and reduce the superfluous informations. but it is a significant disadvantage in the real application that the inference result form is not the real control value but the divided disjoint interval attribute. In order to overcome this difficulty, we will propose approach in which Rough set theory and Neuro-fuzzy fusion are combined to obtain the optimal rule base from lots of input/output datum. These results are applied to the rule construction for infering the temperatures of refrigerator's specified points.

• Journal of the Korean Data and Information Science Society
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• v.14 no.1
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• pp.103-111
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• 2003

Kernel machines are used widely in real-world regression tasks. Kernel ridge regressions(KRR) and support vector machines(SVM) are typical kernel machines. Here, we focus on two types of KRR. One is inductive KRR. The other is transductive KRR. In this paper, we study how differently they work in the interpolation and extrapolation areas. Furthermore, we study prediction interval estimation method for KRR. This turns out to be a reliable and practical measure of prediction interval and is essential in real-world tasks.

• The KIPS Transactions:PartB
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• v.11B no.1
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• pp.83-90
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• 2004

Strategic games are missing special qualities of genre these days. Game engines neither reason about behaviors of computer objects nor have learning ability that can prepare countermeasure in variously command user's strategy. This paper suggests a strategic game engine that applies non-monotonic reasoning and inductive machine learning. The engine emphasizes three components -“user behavior monitor”to abstract user's objects behavior,“learning engine”to learn user's strategy,“behavior display handler”to reflect abstracted behavior of computer objects on game. Especially, this paper proposes two layered-structure to apply non-monotonic reasoning and inductive learning to make behaviors of computer objects that learns strategy behaviors of user objects exactly, and corresponds in user's objects. The engine decides actions and strategies of computer objects with created information through inductive learning. Main contribution of this paper is that computer objects command excellent strategies and reveal differentiation with behavior of existing computer objects to apply non-monotonic reasoning and inductive machine learning.