• IEIE Transactions on Smart Processing and Computing
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• v.3 no.4
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• pp.234-239
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• 2014

This paper presents an empirical study on selecting a small amount of useful unlabeled data to improve the classification accuracy of semi-supervised learning algorithms. In particular, a hybrid method of unifying the simply recycled selection method and the incrementally-reinforced selection method was considered and evaluated empirically. The experimental results, which were obtained from well-known benchmark data sets using semi-supervised support vector machines, demonstrated that the hybrid method works better than the traditional ones in terms of the classification accuracy.

• Phonetics and Speech Sciences
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• v.5 no.2
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• pp.71-79
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• 2013

This study provided longitudinal examination of the Chinese learners' acquisition of the Korean vowels. Specifically the author examined whether Korean monophthongs are acquired rapidly in early stages of learning (Flege, Munro and Skelton, 1992; Munro and Derwing, 2008) or they develop rather gradually in proportion to the learners' experience (Byee, 2001; Ellis, 2006). This study collected the Korean vowel production by 23 Chinese learners for a year, and then analysed F1 and F2 of each Korean vowel. The results showed that 1) Most of the second language (L2) vowels were rapidly improved during the first six or nine months of Korean learning before reaching the constant stage; and 2) The exact acquisition trajectories varied across the seven vowels. Specifically the vowels which were acquired in the early stage of learning were /i, e, ɨ/ for F1 and /ʌ, e, o, u/ for F2. Thus this study supports the hypothesis of Flege et al. (1992) and Munro and Derwing (2008) except the fact that each vowel showed the different learning route.

• Journal of Korean Institute of Industrial Engineers
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• v.32 no.2
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• pp.74-81
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• 2006

In this paper, we deal with the separation of data by concurrently determined, piecewise nonlinear discriminant functions. Toward the end, we develop a new $l_1$-distance norm error metric and cast the problem as a mixed 0-1 integer and linear programming (MILP) model. Given a finite number of discriminant functions as an input, the proposed model considers the synergy as well as the individual role of the functions involved and implements a simplest nonlinear decision surface that best separates the data on hand. Hence, exploiting powerful MILP solvers, the model efficiently analyzes any given data set for its piecewise nonlinear separability. The classification of four sets of artificial data demonstrates the aforementioned strength of the proposed model. Classification results on five machine learning benchmark databases prove that the data separation via the proposed MILP model is an effective supervised learning methodology that compares quite favorably to well-established learning methodologies.

• Journal of Korea Multimedia Society
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• v.9 no.6
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• pp.762-772
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• 2006

Instance based learning algorithm is the best known lazy learner and has been successfully used in many areas such as pattern analysis, medical analysis, bioinformatics and internet applications. However, its feature weighting scheme is too naive that many other extensions are proposed. Our version of IB3 named as eXtended IBL (XIBL) improves feature weighting scheme by backward stepwise regression and its distance function by VDM family that avoids overestimating discrete valued attributes. Also, XIBL adopts leave-one-out as its noise filtering scheme. Experiments with common artificial domains show that XIBL is better than the original IBL in terms of accuracy and noise tolerance. XIBL is applied to two important applications - intrusion detection and spam mail filtering and the results are promising.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1344-1346
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• 1996

In this paper, we discuss an informal analysis of diffusion, global optimization properties of Langevine competitive learning neural network. In the view of the stochastic process, it is important that competitive learning gurantee an optimal solution for pattern recognition. We show that the binary reinforcement function in Langevine competitive learning is a brownian motion as Gaussian process, and construct the Fokker-Plank equation for the proposed neural network. Finally, we show that the informal analysis of the proposed algorithm has a possiblity of globally optimal. solution with the proper initial condition.

• Journal of the Korea Society of Computer and Information
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• v.23 no.9
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• pp.7-13
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• 2018

Recent advancement in data gathering technique improves the capability of information collecting, thus allowing the learning process between gathered data patterns and application sub-tasks. A pattern can be associated with multiple labels, demanding multi-label learning capability, resulting in significant attention to multi-label feature selection since it can improve multi-label learning accuracy. However, existing evolutionary multi-label feature selection methods suffer from ineffective search process. In this study, we propose a evolutionary search process for the task of multi-label feature selection problem. The proposed method creates large set of offspring or new feature subsets and then retains the most promising feature subset. Experimental results demonstrate that the proposed method can identify feature subsets giving good multi-label classification accuracy much faster than conventional methods.

• Educational Technology International
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• v.22 no.2
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• pp.311-339
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• 2021

This study examined the effects of scaffolding types (Toulmin's Argument Pattern: TAP or Argumentation Vee Diagram: AVD) and individual metacognition levels (low or high) on students' learning achievement in online collaborative argumentation. A total of 191 Chinese undergraduates took part in this study. They were randomly assigned to either the TAP scaffolding, AVD scaffolding, or no scaffolding condition. They were teamed up in small groups of two or three students to argue with their peers using SNS as the online collaborative argumentation environment. The results revealed that students in the TAP and AVD scaffolding conditions did not gain significantly higher retention or transfer scores than students without scaffolding. However, students in the TAP scaffolding condition significantly outperformed those in the AVD scaffolding condition on transfer scores. Individual metacognition did not significantly affect learning achievement in online collaborative argumentation. Additionally, there was no significant interaction effect between scaffolding types and individual metacognition levels on retention or on transfer. The findings have implications for scaffolding design for online collaborative argumentation and also suggest that more attention should be paid to social metacognition rather than to individual metacognition when students work collaboratively.

• Journal of the Semiconductor & Display Technology
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• v.21 no.3
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• pp.1-6
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• 2022

Modern computer systems usually have special hardware for operations used in deep learning workload even edge computing environment. Non-volatile memories (NVMs) have been considered for alternative memory storage because they consume little static energy and occupy small area. However, there is a problem for NVMs to be directly adopted. An NVM cell has limited write endurance, so that the lifetime of NVM-based memory system is much shorter than that of conventional memory system. To overcome this problem for the deep learning system, this paper proposes a novel method to extend the lifetime based on the analysis of the deep learning workloads. If an incoming block has more than a predefined number of frequently used values, the cacheline is defined as write friendly block. During the victim selection, the cacheline has lower possibility to be chosen as victim. The experimental results show that the lifetime is increased by about 50% and energy consumption is decreased by 3% with a little performance hurt.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.125-139
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• 2023

Facial expression recognition, a topical problem in the field of computer vision and pattern recognition, is a direct means of recognizing human emotions and behaviors. This paper first summarizes the datasets commonly used for expression recognition and their associated characteristics and presents traditional machine learning algorithms and their benefits and drawbacks from three key techniques of face expression; image pre-processing, feature extraction, and expression classification. Deep learning-oriented expression recognition methods and various algorithmic framework performances are also analyzed and compared. Finally, the current barriers to facial expression recognition and potential developments are highlighted.

• Journal of Service Research and Studies
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• v.3 no.2
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• pp.53-60
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• 2013

Datamining from the large data in the form of various techniques for obtaining information have been developed. In recent years one of the most sought areas of pattern recognition and machine learning method is created with most of existing learning algorithms based on categorical attributes to a rule or decision model. However, the real-world data, it may consist of numeric attributes in many cases. In addition it contains attributes with numerical values to the normal categorical attribute. In this case, therefore, it is required processes in order to use the data to learn an appropriate value for the type attribute. In this paper, the domain of the numeric attributes are divided into several segments using learning algorithm techniques of discritization. It is described Clustering with other data mining techniques. Large amount of first cluster with characteristics is similar records from the database into smaller groups that split multiple given finite patterns in the pattern space. It is close to each other of a set of patterns that together make up a bunch. Among the set without specifying a particular category in a given data by extracting a pattern. It will be described similar grouping of data clustering technique to classify the data.