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

Search Result 26, Processing Time 0.047 seconds

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

  • Kim, Bada;Heo, Junyoung
    • International journal of advanced smart convergence
    • /
    • v.9 no.1
    • /
    • pp.113-120
    • /
    • 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.

Abnormality Detection to Non-linear Multivariate Process Using Supervised Learning Methods (지도학습기법을 이용한 비선형 다변량 공정의 비정상 상태 탐지)

  • Son, Young-Tae;Yun, Deok-Kyun
    • IE interfaces
    • /
    • v.24 no.1
    • /
    • pp.8-14
    • /
    • 2011
  • Principal Component Analysis (PCA) reduces the dimensionality of the process by creating a new set of variables, Principal components (PCs), which attempt to reflect the true underlying process dimension. However, for highly nonlinear processes, this form of monitoring may not be efficient since the process dimensionality can't be represented by a small number of PCs. Examples include the process of semiconductors, pharmaceuticals and chemicals. Nonlinear correlated process variables can be reduced to a set of nonlinear principal components, through the application of Kernel Principal Component Analysis (KPCA). Support Vector Data Description (SVDD) which has roots in a supervised learning theory is a training algorithm based on structural risk minimization. Its control limit does not depend on the distribution, but adapts to the real data. So, in this paper proposes a non-linear process monitoring technique based on supervised learning methods and KPCA. Through simulated examples, it has been shown that the proposed monitoring chart is more effective than $T^2$ chart for nonlinear processes.

Word Sense Similarity Clustering Based on Vector Space Model and HAL (벡터 공간 모델과 HAL에 기초한 단어 의미 유사성 군집)

  • Kim, Dong-Sung
    • Korean Journal of Cognitive Science
    • /
    • v.23 no.3
    • /
    • pp.295-322
    • /
    • 2012
  • In this paper, we cluster similar word senses applying vector space model and HAL (Hyperspace Analog to Language). HAL measures corelation among words through a certain size of context (Lund and Burgess 1996). The similarity measurement between a word pair is cosine similarity based on the vector space model, which reduces distortion of space between high frequency words and low frequency words (Salton et al. 1975, Widdows 2004). We use PCA (Principal Component Analysis) and SVD (Singular Value Decomposition) to reduce a large amount of dimensions caused by similarity matrix. For sense similarity clustering, we adopt supervised and non-supervised learning methods. For non-supervised method, we use clustering. For supervised method, we use SVM (Support Vector Machine), Naive Bayes Classifier, and Maximum Entropy Method.

  • PDF

Decoding Brain States during Auditory Perception by Supervising Unsupervised Learning

  • Porbadnigk, Anne K.;Gornitz, Nico;Kloft, Marius;Muller, Klaus-Robert
    • Journal of Computing Science and Engineering
    • /
    • v.7 no.2
    • /
    • pp.112-121
    • /
    • 2013
  • The last years have seen a rise of interest in using electroencephalography-based brain computer interfacing methodology for investigating non-medical questions, beyond the purpose of communication and control. One of these novel applications is to examine how signal quality is being processed neurally, which is of particular interest for industry, besides providing neuroscientific insights. As for most behavioral experiments in the neurosciences, the assessment of a given stimulus by a subject is required. Based on an EEG study on speech quality of phonemes, we will first discuss the information contained in the neural correlate of this judgement. Typically, this is done by analyzing the data along behavioral responses/labels. However, participants in such complex experiments often guess at the threshold of perception. This leads to labels that are only partly correct, and oftentimes random, which is a problematic scenario for using supervised learning. Therefore, we propose a novel supervised-unsupervised learning scheme, which aims to differentiate true labels from random ones in a data-driven way. We show that this approach provides a more crisp view of the brain states that experimenters are looking for, besides discovering additional brain states to which the classical analysis is blind.

Classification of Korean Ancient Glass Pieces by Pattern Recognition Method (패턴인지법에 의한 한국산 고대 유리제품의 분류)

  • Lee Chul;Czae Myung-Zoon;Kim Seungwon;Kang Hyung Tae;Lee Jong Du
    • Journal of the Korean Chemical Society
    • /
    • v.36 no.1
    • /
    • pp.113-124
    • /
    • 1992
  • The pattern recognition methods of chemometrics have been applied to multivariate data, for which ninety four Korean ancient glass pieces have been determined for 12 elements by neutron activation analysis. For the purpose, principal component analysis and non-linear mapping have been used as the unsupervised learning methods. As the result, the glass samples have been classified into 6 classes. The SIMCA (statistical isolinear multiple component analysis), adopted as a supervised learning method, has been applied to the 6 training set and the test set. The results of the 6 training set were in accord with the results by principal component analysis and non-linear mapping. For test set, 17 of 33 samples were each allocated to one of the 6 training set.

  • PDF

AN OPTIMAL BOOSTING ALGORITHM BASED ON NONLINEAR CONJUGATE GRADIENT METHOD

  • CHOI, JOOYEON;JEONG, BORA;PARK, YESOM;SEO, JIWON;MIN, CHOHONG
    • Journal of the Korean Society for Industrial and Applied Mathematics
    • /
    • v.22 no.1
    • /
    • pp.1-13
    • /
    • 2018
  • Boosting, one of the most successful algorithms for supervised learning, searches the most accurate weighted sum of weak classifiers. The search corresponds to a convex programming with non-negativity and affine constraint. In this article, we propose a novel Conjugate Gradient algorithm with the Modified Polak-Ribiera-Polyak conjugate direction. The convergence of the algorithm is proved and we report its successful applications to boosting.

An Active Co-Training Algorithm for Biomedical Named-Entity Recognition

  • Munkhdalai, Tsendsuren;Li, Meijing;Yun, Unil;Namsrai, Oyun-Erdene;Ryu, Keun Ho
    • Journal of Information Processing Systems
    • /
    • v.8 no.4
    • /
    • pp.575-588
    • /
    • 2012
  • Exploiting unlabeled text data with a relatively small labeled corpus has been an active and challenging research topic in text mining, due to the recent growth of the amount of biomedical literature. Biomedical named-entity recognition is an essential prerequisite task before effective text mining of biomedical literature can begin. This paper proposes an Active Co-Training (ACT) algorithm for biomedical named-entity recognition. ACT is a semi-supervised learning method in which two classifiers based on two different feature sets iteratively learn from informative examples that have been queried from the unlabeled data. We design a new classification problem to measure the informativeness of an example in unlabeled data. In this classification problem, the examples are classified based on a joint view of a feature set to be informative/non-informative to both classifiers. To form the training data for the classification problem, we adopt a query-by-committee method. Therefore, in the ACT, both classifiers are considered to be one committee, which is used on the labeled data to give the informativeness label to each example. The ACT method outperforms the traditional co-training algorithm in terms of f-measure as well as the number of training iterations performed to build a good classification model. The proposed method tends to efficiently exploit a large amount of unlabeled data by selecting a small number of examples having not only useful information but also a comprehensive pattern.

Machine Learning Approach to Blood Stasis Pattern Identification Based on Self-reported Symptoms (기계학습을 적용한 자기보고 증상 기반의 어혈 변증 모델 구축)

  • Kim, Hyunho;Yang, Seung-Bum;Kang, Yeonseok;Park, Young-Bae;Kim, Jae-Hyo
    • Korean Journal of Acupuncture
    • /
    • v.33 no.3
    • /
    • pp.102-113
    • /
    • 2016
  • Objectives : This study is aimed at developing and discussing the prediction model of blood stasis pattern of traditional Korean medicine(TKM) using machine learning algorithms: multiple logistic regression and decision tree model. Methods : First, we reviewed the blood stasis(BS) questionnaires of Korean, Chinese, and Japanese version to make a integrated BS questionnaire of patient-reported outcomes. Through a human subject research, patients-reported BS symptoms data were acquired. Next, experts decisions of 5 Korean medicine doctor were also acquired, and supervised learning models were developed using multiple logistic regression and decision tree. Results : Integrated BS questionnaire with 24 items was developed. Multiple logistic regression models with accuracy of 0.92(male) and 0.95(female) validated by 10-folds cross-validation were constructed. By decision tree modeling methods, male model with 8 decision node and female model with 6 decision node were made. In the both models, symptoms of 'recent physical trauma', 'chest pain', 'numbness', and 'menstrual disorder(female only)' were considered as important factors. Conclusions : Because machine learning, especially supervised learning, can reveal and suggest important or essential factors among the very various symptoms making up a pattern identification, it can be a very useful tool in researching diagnostics of TKM. With a proper patient-reported outcomes or well-structured database, it can also be applied to a pre-screening solutions of healthcare system in Mibyoung stage.

Multimodal Biometrics Recognition from Facial Video with Missing Modalities Using Deep Learning

  • Maity, Sayan;Abdel-Mottaleb, Mohamed;Asfour, Shihab S.
    • Journal of Information Processing Systems
    • /
    • v.16 no.1
    • /
    • pp.6-29
    • /
    • 2020
  • Biometrics identification using multiple modalities has attracted the attention of many researchers as it produces more robust and trustworthy results than single modality biometrics. In this paper, we present a novel multimodal recognition system that trains a deep learning network to automatically learn features after extracting multiple biometric modalities from a single data source, i.e., facial video clips. Utilizing different modalities, i.e., left ear, left profile face, frontal face, right profile face, and right ear, present in the facial video clips, we train supervised denoising auto-encoders to automatically extract robust and non-redundant features. The automatically learned features are then used to train modality specific sparse classifiers to perform the multimodal recognition. Moreover, the proposed technique has proven robust when some of the above modalities were missing during the testing. The proposed system has three main components that are responsible for detection, which consists of modality specific detectors to automatically detect images of different modalities present in facial video clips; feature selection, which uses supervised denoising sparse auto-encoders network to capture discriminative representations that are robust to the illumination and pose variations; and classification, which consists of a set of modality specific sparse representation classifiers for unimodal recognition, followed by score level fusion of the recognition results of the available modalities. Experiments conducted on the constrained facial video dataset (WVU) and the unconstrained facial video dataset (HONDA/UCSD), resulted in a 99.17% and 97.14% Rank-1 recognition rates, respectively. The multimodal recognition accuracy demonstrates the superiority and robustness of the proposed approach irrespective of the illumination, non-planar movement, and pose variations present in the video clips even in the situation of missing modalities.

The Adaptation Controller Plan for a Transient State Efficiency Improvement (과도상태 성능 개선을 위한 적응 제어기 설계)

  • Cho, Hyun-Seob;Jun, Ho-Ik
    • Proceedings of the KAIS Fall Conference
    • /
    • /
    • pp.379-381
    • /
    • 2011
  • Dynamic Neural Unit(DNU) based upon the topology of a reverberating circuit in a neuronal pool of the central nervous system. In this thesis, we present a genetic DNU-control scheme for unknown nonlinear systems. Our methodis different from those using supervised learning algorithms, such as the backpropagation (BP) algorithm, that needs training information in each step. The contributions of this thesis are the new approach to constructing neural network architecture and its trainin.

  • PDF