• Title, Summary, Keyword: Semi-Supervised learning

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Semi-supervised Learning for the Positioning of a Smartphone-based Robot (스마트폰 로봇의 위치 인식을 위한 준 지도식 학습 기법)

  • Yoo, Jaehyun;Kim, H. Jin
    • Journal of Institute of Control, Robotics and Systems
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    • v.21 no.6
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    • pp.565-570
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    • 2015
  • Supervised machine learning has become popular in discovering context descriptions from sensor data. However, collecting a large amount of labeled training data in order to guarantee good performance requires a great deal of expense and time. For this reason, semi-supervised learning has recently been developed due to its superior performance despite using only a small number of labeled data. In the existing semi-supervised learning algorithms, unlabeled data are used to build a graph Laplacian in order to represent an intrinsic data geometry. In this paper, we represent the unlabeled data as the spatial-temporal dataset by considering smoothly moving objects over time and space. The developed algorithm is evaluated for position estimation of a smartphone-based robot. In comparison with other state-of-art semi-supervised learning, our algorithm performs more accurate location estimates.

Semi-Supervised Learning Using Kernel Estimation

  • Seok, Kyung-Ha
    • Journal of the Korean Data and Information Science Society
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    • v.18 no.3
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    • pp.629-636
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    • 2007
  • A kernel type semi-supervised estimate is proposed. The proposed estimate is based on the penalized least squares loss and the principle of Gaussian Random Fields Model. As a result, we can estimate the label of new unlabeled data without re-computation of the algorithm that is different from the existing transductive semi-supervised learning. Also our estimate is viewed as a general form of Gaussian Random Fields Model. We give experimental evidence suggesting that our estimate is able to use unlabeled data effectively and yields good classification.

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Semi-supervised regression based on support vector machine

  • Seok, Kyungha
    • Journal of the Korean Data and Information Science Society
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    • v.25 no.2
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    • pp.447-454
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    • 2014
  • In many practical machine learning and data mining applications, unlabeled training examples are readily available but labeled ones are fairly expensive to obtain. Therefore semi-supervised learning algorithms have attracted much attentions. However, previous research mainly focuses on classication problems. In this paper, a semi-supervised regression method based on support vector regression (SVR) formulation that is proposed. The estimator is easily obtained via the dual formulation of the optimization problem. The experimental results with simulated and real data suggest superior performance of the our proposed method compared with standard SVR.

High Efficiency Adaptive Facial Expression Recognition based on Incremental Active Semi-Supervised Learning (점진적 능동준지도 학습 기반 고효율 적응적 얼굴 표정 인식)

  • Kim, Jin-Woo;Rhee, Phill-Kyu
    • The Journal of The Institute of Internet, Broadcasting and Communication
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    • v.17 no.2
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    • pp.165-171
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    • 2017
  • It is difficult to recognize Human's facial expression in the real-world. For these reason, when database and test data have similar condition, we can accomplish high accuracy. Solving these problem, we need to many facial expression data. In this paper, we propose the algorithm for gathering many facial expression data within various environment and gaining high accuracy quickly. This algorithm is training initial model with the ASSL (Active Semi-Supervised Learning) using deep learning network, thereafter gathering unlabeled facial expression data and repeating this process. Through using the ASSL, we gain proper data and high accuracy with less labor force.

Software Fault Prediction using Semi-supervised Learning Methods (세미감독형 학습 기법을 사용한 소프트웨어 결함 예측)

  • Hong, Euyseok
    • The Journal of The Institute of Internet, Broadcasting and Communication
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    • v.19 no.3
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    • pp.127-133
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    • 2019
  • Most studies of software fault prediction have been about supervised learning models that use only labeled training data. Although supervised learning usually shows high prediction performance, most development groups do not have sufficient labeled data. Unsupervised learning models that use only unlabeled data for training are difficult to build and show poor performance. Semi-supervised learning models that use both labeled data and unlabeled data can solve these problems. Self-training technique requires the fewest assumptions and constraints among semi-supervised techniques. In this paper, we implemented several models using self-training algorithms and evaluated them using Accuracy and AUC. As a result, YATSI showed the best performance.

Semi-supervised Multi-view Manifold Discriminant Intact Space Learning

  • Han, Lu;Wu, Fei;Jing, Xiao-Yuan
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.12 no.9
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    • pp.4317-4335
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    • 2018
  • Semi-supervised multi-view latent space learning is gaining considerable popularity recently in many machine learning applications due to the high cost and difficulty to obtain the large amount of label information of data. Although some semi-supervised multi-view latent space learning methods have been presented, there is still much space for improvement: 1) How to learn latent discriminant intact feature representations by employing data of multiple views; 2) How to exploit the manifold structure of both labeled and unlabeled point in the learned latent intact space effectively. To address the above issues, we propose an approach called semi-supervised multi-view manifold discriminant intact space learning ($SM^2DIS$) for image classification in this paper. $SM^2DIS$ aims to seek a manifold discriminant intact space for data of different views by making use of both the discriminant information of labeled data and the manifold structure of both labeled and unlabeled data. Experimental results on MNIST, COIL-20, Multi-PIE, and Caltech-101 databases demonstrate the effectiveness and robustness of our proposed approach.

Semi-Supervised Learning Based Anomaly Detection for License Plate OCR in Real Time Video

  • Kim, Bada;Heo, Junyoung
    • International journal of advanced smart convergence
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    • v.9 no.1
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    • pp.113-120
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    • 2020
  • Recently, the license plate OCR system has been commercialized in a variety of fields and preferred utilizing low-cost embedded systems using only cameras. This system has a high recognition rate of about 98% or more for the environments such as parking lots where non-vehicle is restricted; however, the environments where non-vehicle objects are not restricted, the recognition rate is about 50% to 70%. This low performance is due to the changes in the environment by non-vehicle objects in real-time situations that occur anomaly data which is similar to the license plates. In this paper, we implement the appropriate anomaly detection based on semi-supervised learning for the license plate OCR system in the real-time environment where the appearance of non-vehicle objects is not restricted. In the experiment, we compare systems which anomaly detection is not implemented in the preceding research with the proposed system in this paper. As a result, the systems which anomaly detection is not implemented had a recognition rate of 77%; however, the systems with the semi-supervised learning based on anomaly detection had 88% of recognition rate. Using the techniques of anomaly detection based on the semi-supervised learning was effective in detecting anomaly data and it was helpful to improve the recognition rate of real-time situations.

A Clustering-based Semi-Supervised Learning through Initial Prediction of Unlabeled Data (미분류 데이터의 초기예측을 통한 군집기반의 부분지도 학습방법)

  • Kim, Eung-Ku;Jun, Chi-Hyuck
    • Journal of the Korean Operations Research and Management Science Society
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    • v.33 no.3
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    • pp.93-105
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    • 2008
  • Semi-supervised learning uses a small amount of labeled data to predict labels of unlabeled data as well as to improve clustering performance, whereas unsupervised learning analyzes only unlabeled data for clustering purpose. We propose a new clustering-based semi-supervised learning method by reflecting the initial predicted labels of unlabeled data on the objective function. The initial prediction should be done in terms of a discrete probability distribution through a classification method using labeled data. As a result, clusters are formed and labels of unlabeled data are predicted according to the Information of labeled data in the same cluster. We evaluate and compare the performance of the proposed method in terms of classification errors through numerical experiments with blinded labeled data.

Active Selection of Label Data for Semi-Supervised Learning Algorithm (준감독 학습 알고리즘을 위한 능동적 레이블 데이터 선택)

  • Han, Ji-Ho;Park, Eun-Ae;Park, Dong-Chul;Lee, Yunsik;Min, Soo-Young
    • Journal of IKEEE
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    • v.17 no.3
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    • pp.254-259
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    • 2013
  • The choice of labeled data in semi-supervised learning algorithm can result in effects on the performance of the resultant classifier. In order to select labeled data required for the training of a semi-supervised learning algorithm, VCNN(Vector Centroid Neural Network) is proposed in this paper. The proposed selection method of label data is evaluated on UCI dataset and caltech dataset. Experiments and results show that the proposed selection method outperforms conventional methods in terms of classification accuracy and minimum error rate.

Semi-supervised learning using similarity and dissimilarity

  • Seok, Kyung-Ha
    • Journal of the Korean Data and Information Science Society
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    • v.22 no.1
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    • pp.99-105
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    • 2011
  • We propose a semi-supervised learning algorithm based on a form of regularization that incorporates similarity and dissimilarity penalty terms. Our approach uses a graph-based encoding of similarity and dissimilarity. We also present a model-selection method which employs cross-validation techniques to choose hyperparameters which affect the performance of the proposed method. Simulations using two types of dat sets demonstrate that the proposed method is promising.