• Title/Summary/Keyword: cross validation

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Prediction of Soluble Solids Content of Chestnut using VIS/NIR Spectroscopy

  • Park, Soo Hyun;Lim, Ki Taek;Lee, Hoyoung;Lee, Soo Hee;Noh, Sang Ha
    • Journal of Biosystems Engineering
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    • v.38 no.3
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    • pp.185-191
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    • 2013
  • Purpose: The present study focused on the estimation of soluble solids content (SSC) of chestnut using reflectance and transmittance spectra in range of VIS/NIR. Methods: Four species intact/peeled chestnuts were used for acquisition of spectral data. Transmittance and reflectance spectra were used to develop the best PLS model to estimate SSC of chestnut. Results: The model developed with the transmitted energy spectra of peeled chestnuts rather than intact chestnuts and with range of NIR rather than VIS performed better. The best $R^2$ and RMSEP of cross validation were represented as 0.54 and $1.85^{\circ}Brix$. The results presented that the reflectance spectra of peeled chestnuts by species showed the best performance to predict SSC of chestnut. $R^2$ and RMSEP were 0.55 and $1.67^{\circ}Brix$. Conclusions: All developed models showed RMSEP around $1.44{\sim}2.54^{\circ}Brix$, which is considered not enough to estimate SSC accurately. It was noted that $R^2$ of cross validation that we found were not high. For all that, grading of the fruits in two or three classes of SSC during postharvest handling seems possible with an inexpensive spectrophotometer. Furthermore, the development of estimation of SSC by each chestnut species could be considered in that SSC distribution is clustering in different range by species.

Predicting Early Retirees Using Personality Data (인성 데이터를 활용한 조기 퇴사자 예측)

  • Kim, Young Park;Kim, Hyoung Joong
    • Journal of Digital Contents Society
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    • v.19 no.1
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    • pp.141-147
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    • 2018
  • This study analyzed the early retired employees who stayed in company no longer than 3 years based on a certain company's personality evaluation result data. The predicted model was analyzed by dividing into two categories; the manufacture group and the R&D group. Independent variables were selected according to the stepwise method. A logistic regression model was selected as a prediction model among various supervised learning methods, and trained through cross-validation to prevent over-fitting or under-fitting. The accuracy of the two groups were confirmed by the confusion matrix. The most influential factor for early retirement in the manufacture group was revealed as "immersion," and for the R&D group appeared as "antisocial." In the past, people concentrated on collecting data by questionnaire and identifying factors that are highly related to the retirement, but this study suggests a sustainable early retirement prediction model in the future by analyzing the tangible outcome of the recruitment process.

Radiometric Cross Validation of KOMPSAT-3 AEISS (다목적실용위성 3호 AEISS센서의 방사 특성 교차 검증)

  • Shin, Dong-yoon;Choi, Chul-uong;Lee, Sun-gu;Ahn, Ho-yong
    • Korean Journal of Remote Sensing
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    • v.32 no.5
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    • pp.529-538
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    • 2016
  • This study, multispectral and hyperspectral sensors were utilized to use radiometric cross validation for the purpose of radiometric quality evaluation of a 'KOMPSAT-3'. Images of EO-1 Hyperion and Landsat-8 OLI sensors taken in PICS site were used. 2 sections that have 2 different types of ground coverage respectively were selected as the site of cross validation based on aerial hyperspectral sensor and TOA Reflectance. As a result of comparison between the TOA reflectance figures of KOMPSAT-3, EO-1 Hyperion and CASI-1500, the difference was roughly 4%. It is considered that it satisfies the radiological quality standard when the difference of figure of reflectance in a comparison to the other satellites is found within 5%. The difference in Blue, Green, Red band was approximately 3% as a comparison result of TOA reflectance. However the figure was relatively low in NIR band in a comparison to Landsat-8. It is thought that the relatively low reflectance is because there is a difference of band passes in NIR band of 2 sensors and in a case of KOMPSAT-3 sensor, a section of 940nm, which shows the strong absorption through water vapor, is included in band pass resulting in comparatively low reflectance. To overcome these conditions, more detailed analysis with the application of rescale method as Spectral Bandwidth Adjustment Factor (SBAF) is required.

Detecting Errors in POS-Tagged Corpus on XGBoost and Cross Validation (XGBoost와 교차검증을 이용한 품사부착말뭉치에서의 오류 탐지)

  • Choi, Min-Seok;Kim, Chang-Hyun;Park, Ho-Min;Cheon, Min-Ah;Yoon, Ho;Namgoong, Young;Kim, Jae-Kyun;Kim, Jae-Hoon
    • KIPS Transactions on Software and Data Engineering
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    • v.9 no.7
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    • pp.221-228
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    • 2020
  • Part-of-Speech (POS) tagged corpus is a collection of electronic text in which each word is annotated with a tag as the corresponding POS and is widely used for various training data for natural language processing. The training data generally assumes that there are no errors, but in reality they include various types of errors, which cause performance degradation of systems trained using the data. To alleviate this problem, we propose a novel method for detecting errors in the existing POS tagged corpus using the classifier of XGBoost and cross-validation as evaluation techniques. We first train a classifier of a POS tagger using the POS-tagged corpus with some errors and then detect errors from the POS-tagged corpus using cross-validation, but the classifier cannot detect errors because there is no training data for detecting POS tagged errors. We thus detect errors by comparing the outputs (probabilities of POS) of the classifier, adjusting hyperparameters. The hyperparameters is estimated by a small scale error-tagged corpus, in which text is sampled from a POS-tagged corpus and which is marked up POS errors by experts. In this paper, we use recall and precision as evaluation metrics which are widely used in information retrieval. We have shown that the proposed method is valid by comparing two distributions of the sample (the error-tagged corpus) and the population (the POS-tagged corpus) because all detected errors cannot be checked. In the near future, we will apply the proposed method to a dependency tree-tagged corpus and a semantic role tagged corpus.

Buckling and vibration of symmetric laminated composite plates with edges elastically restrained

  • Ashour, Ahmed S.
    • Steel and Composite Structures
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    • v.3 no.6
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    • pp.439-450
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    • 2003
  • The finite strip transition matrix technique, a semi analytical method, is employed to obtain the buckling loads and the natural frequencies of symmetric cross-ply laminated composite plates with edges elastically restrained against both translation and rotation. To illustrate the accuracy and the validation of the method several example of cross play laminated composite plates were analyzed. The buckling loads and the frequency parameters are presented and compared with available results in the literature. The convergence study and the excellent agreement with known results show the reliability of the purposed technique.

Basic Aspects of Signal Processing in Ultrasonic Imaging

  • Saito, Masao
    • Journal of Biomedical Engineering Research
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    • v.5 no.1
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    • pp.5-8
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    • 1984
  • As the ensemble averaged dz/dt signal during exercise gets smoothed, it is difficult to find the distinctive marks for estimation of stroke volume. The cross correlation function was made use of estmating these marks for automatic calculation by computer from the ensemble averaged dz/dt signal. LVET(Left Ventricular Ejection Time) and stroke volume were estimated based on the calculated parameters from the characteristic points. LVET, stroke volume calculated by hand, by the ensemble average and the cross correlation were compared for accuracy validation.

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A Simple Bias-Correction Rule for the Apparent Prediction Error

  • Beong-Soo So
    • Communications for Statistical Applications and Methods
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    • v.2 no.2
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    • pp.146-154
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    • 1995
  • By using simple Taylor expansion, we derive an easy bias-correction rule for the apparent prodiction error of the predictor defined by the general M-estimators with respect to an arbitrary measure of prediction error. Our method has a considerable computational advantage over the previous methods based on the resampling thchnique such as Cross-validaton and Boothtrap. Connections with AIC, Cross-Validation and Boothtrap are discussed too.

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Validation of Korean Version of Multidimensional Students' Life Satisfaction Scale (한국판 아동용 다면적 생활만족도 척도(K-MSLSS)의 타당화 연구)

  • Lee, Jeong Mi;Lee, Yanghee
    • Korean Journal of Child Studies
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    • v.29 no.4
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    • pp.249-268
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    • 2008
  • The Korean version of the Multidimensional Students' Life Satisfaction Scale for children(K-MSLSS) assesses children's subjective perceptions of Life Satisfaction(LS) in five conceptually relevant domains : Friends, School, Family, Living Environment, and Self. The purpose of the present study was to validate the five-factor structure of the K-MSLSS using Confirmatory Factor Analysis(CFA) procedures by means of the AMOS 7.0 statistical program. Of the 681 children(10.5 years, SD=1.1) recruited from three public elementary schools in Seoul 431 children were the calibration sample and 430 children were the validation sample. Results of the analyses found that the five-factor structure of K-MSLSS is applicable for use with Korean children from 9-12 years of age regardless of gender.

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Virtual Community Recommendation Model using Technology Acceptance Model and User's Needs Type (기술수용모형과 사용자의 욕구유형을 활용한 가상 커뮤니티 추천 모형)

  • Lee, Hyoung-Yong;Han, In-Goo;Ahn, Hyun-Chul
    • Asia pacific journal of information systems
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    • v.16 no.4
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    • pp.217-238
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    • 2006
  • In this study, we propose a virtual community recommendation model based on user behavioral models. It is designed to recommend optimal virtual communities for an active user by applying case-based reasoning (CBR) using behavioral factors suggested in the technology acceptance model (TAM) and its extensions. Also, it is designed to filter its case-base by considering the user's needs type before applying CBR. To test the usefulness of our model, we conduct two-step validation - experimental validation for the collected data, and survey validation for investigating the actual satisfaction level. Experimental results show that our model presents effective recommendation results in an efficient way. In addition, they also show that the information on the user's needs type may generate opportunities for cross-selling other commercial items.

A study for development and validation of the 'course evaluation' scale for learner-centered (학습자 중심의 '강의평가' 도구 개발 및 타당화 연구)

  • Park, Sung-Mi
    • Journal of Fisheries and Marine Sciences Education
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    • v.23 no.1
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    • pp.13-22
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    • 2011
  • The purpose of this study was to development and validation of the 'course evaluation' scale for learner-centered in university. The research collected preliminary data from 1,567 university students's responses for item and scale quality analyses, and collected 2,539 university students's for item and scale quality analyses, and 300 university professors's responses for validation. Data were analyzed to obtain item quality, reliability, and validity analysis. The results of the study were as follows; The 'course evaluation' scale for learner-centered in university was defined by 5 factors. The 5 factors were structure and sincerity of lecture, suitability of report and test, level of consulting for student, application of educational media, communication. The results of the confirmatory factor analysis confirmed five sub-scales in the 'course evaluation' scale for learner-centered in university scale. Criterion-related validity evidence was obtained from the correlation analysis as the criterion measures. Cross validity evidence was obtained from the confirmatory factor analysis in university professors.