Our work towards a Structured Evaluation Methodology for Artificial Intelligence Technology (SEMAIT) aims to provide plots, tools, methods, and strategies to extract insights out of various machine learning (ML) and Artificial Intelligence (AI) data.Included in this software is the MIg analyZeR (mizr) R software package that produces various plots. It was initially developed within the Multimodal Information Group (MIG) at the National Institute of Standards and Technology (NIST).This software is documented, configured to be installed as an R package, and comes with an example SEMAIT script with an example (system, dataset, metrics, score) ML tuple set that we constructed ourselves.

Our work towards a Structured Evaluation Methodology for Artificial Intelligence Technology (SEMAIT) aims to provide plots, tools, methods, and strategies to extract insights out of various machine learning (ML) and Artificial Intelligence (AI) data.Included in this software is the MIg analyZeR (mizr) R software package that produces various plots. It was initially developed within the Multimodal Information Group (MIG) at the National Institute of Standards and Technology (NIST).This software is documented, configured to be installed as an R package, and comes with an example SEMAIT script with an example (system, dataset, metrics, score) ML tuple set that we constructed ourselves.

경기도 경기데이터분석포털의 분석교육 강의정보 목록입니다. 경기데이터분석포털>소통참여>분석교육 에서도 확인 가능합니다.

Broad classes of statistical classification algorithms have beendeveloped and applied successfully to a wide range of real worlddomains. In general, ensuring that the particular classificationalgorithm matches the properties of the data is crucial inproviding results that meet the needs of the particular applicationdomain. One way in which the impact of this algorithm/applicationmatch can be alleviated is by using ensembles of classifiers, wherea variety of classifiers (either different types of classifiers ordifferent instantiations of the same classifier) are pooled before afinal classification decision is made. Intuitively, classifierensembles allow the different needs of a difficult problem to behandled by classifiers suited to those particular needs.Mathematically, classifier ensembles provide an extra degree offreedom in the classical bias/variance tradeoff, allowing solutionsthat would be difficult (if not impossible) to reach with only asingle classifier. Because of these advantages, classifier ensembles have been applied to many difficult real world problems. In this paper, we surveyselect applications of ensemble methods to problems that havehistorically been most representative of the difficulties inclassification. In particular, we survey applications of ensemblemethods to remote sensing, person recognition, one vs. allrecognition, and medicine.

SPATIALLY ADAPTIVE SEMI-SUPERVISED LEARNING WITH GAUSSIAN PROCESSES FOR HYPERSPECTRAL DATA ANALYSIS GOO JUN * AND JOYDEEP GHOSH* Abstract. A semi-supervised learning algorithm for the classification of hyperspectral data, Gaussian process expectation maximization (GP-EM), is proposed. Model parameters for each land cover class is first estimated by a supervised algorithm using Gaussian process regressions to find spatially adaptive parameters, and the estimated parameters are then used to initialize a spatially adaptive mixture-of-Gaussians model. The mixture model is updated by expectationmaximization iterations using the unlabeled data, and the spatially adaptive parameters for unlabeled instances are obtained by Gaussian process regressions with soft assignments. Two sets of hyperspectral data taken from the Botswana area by the NASA EO-1 satellite are used for experiments. Empirical evaluations show that the proposed framework performs significantly better than baseline algorithms that do not use spatial information, and the results are also better than any previously reported results by other algorithms on the same data.

교육시스템_운영중인 교육과정_마스터에 대한 데이터로 과정코드, 과정명, 과정개요, 교육대상 학습환경 등에 대한 항목들을 제공합니다.