• Title, Summary, Keyword: Supervised/Non-supervised Learning

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Introduction to convolutional neural network using Keras; an understanding from a statistician

  • Lee, Hagyeong;Song, Jongwoo
    • Communications for Statistical Applications and Methods
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    • v.26 no.6
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    • pp.591-610
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    • 2019
  • Deep Learning is one of the machine learning methods to find features from a huge data using non-linear transformation. It is now commonly used for supervised learning in many fields. In particular, Convolutional Neural Network (CNN) is the best technique for the image classification since 2012. For users who consider deep learning models for real-world applications, Keras is a popular API for neural networks written in Python and also can be used in R. We try examine the parameter estimation procedures of Deep Neural Network and structures of CNN models from basics to advanced techniques. We also try to figure out some crucial steps in CNN that can improve image classification performance in the CIFAR10 dataset using Keras. We found that several stacks of convolutional layers and batch normalization could improve prediction performance. We also compared image classification performances with other machine learning methods, including K-Nearest Neighbors (K-NN), Random Forest, and XGBoost, in both MNIST and CIFAR10 dataset.

Study on Structure Visual Inspection Technology using Drones and Image Analysis Techniques (드론과 이미지 분석기법을 활용한 구조물 외관점검 기술 연구)

  • Kim, Jong-Woo;Jung, Young-Woo;Rhim, Hong-Chul
    • Journal of the Korea Institute of Building Construction
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    • v.17 no.6
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    • pp.545-557
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    • 2017
  • The study is about the efficient alternative to concrete surface in the field of visual inspection technology for deteriorated infrastructure. By combining industrial drones and deep learning based image analysis techniques with traditional visual inspection and research, we tried to reduce manpowers, time requirements and costs, and to overcome the height and dome structures. On board device mounted on drones is consisting of a high resolution camera for detecting cracks of more than 0.3 mm, a lidar sensor and a embeded image processor module. It was mounted on an industrial drones, took sample images of damage from the site specimen through automatic flight navigation. In addition, the damege parts of the site specimen was used to measure not only the width and length of cracks but white rust also, and tried up compare them with the final image analysis detected results. Using the image analysis techniques, the damages of 54ea sample images were analyzed by the segmentation - feature extraction - decision making process, and extracted the analysis parameters using supervised mode of the deep learning platform. The image analysis of newly added non-supervised 60ea image samples was performed based on the extracted parameters. The result presented in 90.5 % of the damage detection rate.

A Study on the Abnormal Behavior Detection Model through Data Transfer Data Analysis (자료 전송 데이터 분석을 통한 이상 행위 탐지 모델의 관한 연구)

  • Son, In Jae;Kim, Huy Kang
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.30 no.4
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    • pp.647-656
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    • 2020
  • Recently, there has been an increasing number of cases in which important data (personal information, technology, etc.) of national and public institutions are leaked to the outside world. Surveys show that the largest cause of such leakage accidents is "insiders." Insiders of organization with the most authority can cause more damage than technology leaks caused by external attacks due to the organization. This is due to the characteristics of insiders who have relatively easy access to the organization's major assets. This study aims to present an optimized property selection model for detecting such abnormalities through supervised learning algorithms among machine learning techniques using actual data such as CrossNet data transfer system transmission log, e-mail transmission log, and personnel information, which safely transmits data between separate areas (security area and non-security area) of the business network and the Internet network.

Domain Adaptation for Opinion Classification: A Self-Training Approach

  • Yu, Ning
    • Journal of Information Science Theory and Practice
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    • v.1 no.1
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    • pp.10-26
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    • 2013
  • Domain transfer is a widely recognized problem for machine learning algorithms because models built upon one data domain generally do not perform well in another data domain. This is especially a challenge for tasks such as opinion classification, which often has to deal with insufficient quantities of labeled data. This study investigates the feasibility of self-training in dealing with the domain transfer problem in opinion classification via leveraging labeled data in non-target data domain(s) and unlabeled data in the target-domain. Specifically, self-training is evaluated for effectiveness in sparse data situations and feasibility for domain adaptation in opinion classification. Three types of Web content are tested: edited news articles, semi-structured movie reviews, and the informal and unstructured content of the blogosphere. Findings of this study suggest that, when there are limited labeled data, self-training is a promising approach for opinion classification, although the contributions vary across data domains. Significant improvement was demonstrated for the most challenging data domain-the blogosphere-when a domain transfer-based self-training strategy was implemented.

The Informative Support and Emotional Support Classification Model for Medical Web Forums using Text Analysis (의료 웹포럼에서의 텍스트 분석을 통한 정보적 지지 및 감성적 지지 유형의 글 분류 모델)

  • Woo, Jiyoung;Lee, Min-Jung;Ku, Yungchang
    • Journal of Information Technology Services
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    • v.11 no.sup
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    • pp.139-152
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    • 2012
  • In the medical web forum, people share medical experience and information as patients and patents' families. Some people search medical information written in non-expert language and some people offer words of comport to who are suffering from diseases. Medical web forums play a role of the informative support and the emotional support. We propose the automatic classification model of articles in the medical web forum into the information support and emotional support. We extract text features of articles in web forum using text mining techniques from the perspective of linguistics and then perform supervised learning to classify texts into the information support and the emotional support types. We adopt the Support Vector Machine (SVM), Naive-Bayesian, decision tree for automatic classification. We apply the proposed model to the HealthBoards forum, which is also one of the largest and most dynamic medical web forum.

Systematic Approach for Detecting Text in Images Using Supervised Learning

  • Nguyen, Minh Hieu;Lee, GueeSang
    • International Journal of Contents
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    • v.9 no.2
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    • pp.8-13
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    • 2013
  • Locating text data in images automatically has been a challenging task. In this approach, we build a three stage system for text detection purpose. This system utilizes tensor voting and Completed Local Binary Pattern (CLBP) to classify text and non-text regions. While tensor voting generates the text line information, which is very useful for localizing candidate text regions, the Nearest Neighbor classifier trained on discriminative features obtained by the CLBP-based operator is used to refine the results. The whole algorithm is implemented in MATLAB and applied to all images of ICDAR 2011 Robust Reading Competition data set. Experiments show the promising performance of this method.

Acoustic Emission Source Classification of Finite-width Plate with a Circular Hole Defect using k-Nearest Neighbor Algorithm (k-최근접 이웃 알고리즘을 이용한 원공결함을 갖는 유한 폭 판재의 음향방출 음원분류에 대한 연구)

  • Rhee, Zhang-Kyu;Oh, Jin-Soo
    • Journal of the Korea Safety Management and Science
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    • v.11 no.1
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    • pp.27-33
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    • 2009
  • A study of fracture to material is getting interest in nuclear and aerospace industry as a viewpoint of safety. Acoustic emission (AE) is a non-destructive testing and new technology to evaluate safety on structures. In previous research continuously, all tensile tests on the pre-defected coupons were performed using the universal testing machine, which machine crosshead was move at a constant speed of 5mm/min. This study is to evaluate an AE source characterization of SM45C steel by using k-nearest neighbor classifier, k-NNC. For this, we used K-means clustering as an unsupervised learning method for obtained multi -variate AE main data sets, and we applied k-NNC as a supervised learning pattern recognition algorithm for obtained multi-variate AE working data sets. As a result, the criteria of Wilk's $\lambda$, D&B(Rij) & Tou are discussed.

QSO Selections Using Time Variability and Machine Learning

  • Kim, Dae-Won;Protopapas, Pavlos;Byun, Yong-Ik;Alcock, Charles;Khardon, Roni
    • The Bulletin of The Korean Astronomical Society
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    • v.36 no.2
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    • pp.64-64
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    • 2011
  • We present a new quasi-stellar object (QSO) selection algorithm using a Support Vector Machine, a supervised classification method, on a set of extracted time series features including period, amplitude, color, and autocorrelation value. We train a model that separates QSOs from variable stars, non-variable stars, and microlensing events using 58 known QSOs, 1629 variable stars, and 4288 non-variables in the MAssive Compact Halo Object (MACHO) database as a training set. To estimate the efficiency and the accuracy of the model, we perform a cross-validation test using the training set. The test shows that the model correctly identifies ~80% of known QSOs with a 25% false-positive rate. The majority of the false positives are Be stars. We applied the trained model to the MACHO Large Magellanic Cloud (LMC) data set, which consists of 40 million lightcurves, and found 1620 QSO candidates. During the selection, none of the 33,242 known MACHO variables were misclassified as QSO candidates. In order to estimate the true false-positive rate, we crossmatched the candidates with astronomical catalogs including the Spitzer Surveying the Agents of a Galaxy's Evolution (SAGE) LMC catalog and a few X-ray catalogs. The results further suggest that the majority of the candidates, more than 70%, are QSOs.

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Development of Image Defect Detection Model Using Machine Learning (기계 학습을 활용한 이미지 결함 검출 모델 개발)

  • Lee, Nam-Yeong;Cho, Hyug-Hyun;Ceong, Hyi-Thaek
    • The Journal of the Korea institute of electronic communication sciences
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    • v.15 no.3
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    • pp.513-520
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    • 2020
  • Recently, the development of a vision inspection system using machine learning has become more active. This study seeks to develop a defect inspection model using machine learning. Defect detection problems for images correspond to classification problems, which are the method of supervised learning in machine learning. In this study, defect detection models are developed based on algorithms that automatically extract features and algorithms that do not extract features. One-dimensional CNN and two-dimensional CNN are used as algorithms for automatic extraction of features, and MLP and SVM are used as algorithms for non-extracting features. A defect detection model is developed based on four models and their accuracy and AUC compare based on AUC. Although image classification is common in the development of models using CNN, high accuracy and AUC is achieved when developing SVM models by converting pixels from images into RGB values in this study.