Results 291 to 300 of about 770,485 (338)
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R Symbolic Data Analysis for Symbolic Artificial Intelligence
Journal of Korean Institute of Intelligent Systems, 2017컴퓨터와 인간은 분명 다르지만 기본적으로 데이터를 저장하고 처리하는 개념적 측면에서는 서로 유사한 구조를 갖는다. 하지만 수집된 전체 데이터를 처리하고 분석하는 컴퓨터와는 달리 인간은 요약된 패턴 단위로 데이터를 처리한다. 즉 인간은 전체 데이터를 다루기보다는 요약된 정보를 통해 최적의 의사결정을 한다. 전체 데이터보다 요약된 정보만을 관리하면 시간과 비용 면에서 더 효율적인 시스템을 구축할 수 있다. 특히 빅데이터 환경에서 인공지능의 학습을 위한 대용량 데이터의 처리 및 분석을 위하여 요약된 정보에 기반 한 데이터학습에 대한 필요성이 제기되고 있다.
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Visualizing Symbolic Data by Closed Shapes
2003In the framework of Factorial Data Analysis on Symbolic Objects (SO’s), we propose new kinds of SO’s visualizations on factorial planes alternative to rectangular shapes (Minimum Covering Area Rectangle MCAR). MCAR were mainly proposed in PCA on SO’s to represent in reduced bi-dimensional subspace symbolic data described by interval variables and ...
IRPINO, Antonio +2 more
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Descriptive Statistics for Symbolic Data
2000The intention of this chapter is to extend the concept of frequency distribution, and the standard definitions of descriptive statistics for real-valued data, such as the empirical mean the empirical standard deviation and the median, to the general framework of symbolic variables.
Bertrand, Patrice +1 more
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Dimensionality reduction of symbolic data
Pattern Recognition Letters, 1995Abstract Hitherto dimensionality/feature reduction techniques are studied with reference to conventional data, where the objects are represented by numerical vectors. This proposal is to extend the notion of dimensionality reduction to more generalised objects called Symbolic data. A mathematical model which achieves generation of symbolic features
P. Nagabhushan +2 more
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Fuzzy clustering for symbolic data
IEEE Transactions on Fuzzy Systems, 1998Most of the techniques used in the literature in clustering symbolic data are based on the hierarchical methodology, which utilizes the concept of agglomerative or divisive methods as the core of the algorithm. The main contribution of this paper is to show how to apply the concept of fuzziness on a data set of symbolic objects and how to use this ...
Y. El-Sonbaty, M.A. Ismail
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Histograms in symbolic data analysis
Annals of Operations Research, 1995zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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DATA DIFFERENTIATION AND CARTOGRAPHIC SYMBOLIZATION
Cartographica, 1976The study examines three types of data differentiation and the relationships between them and cartographic symbolization. The differentiation of point, line, area, and volume data generally corresponds to the use of point, line, and area symbols. There are exceptions to the general rule, of which the use of point symbols for area data in aggregate ...
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An overview of real‐world data sources for oncology and considerations for research
Ca-A Cancer Journal for Clinicians, 2022Lynne Penberthy +2 more
exaly
Performance Estimation Using Symbolic Data
2013Symbolic execution is a useful technique in formal verification and testing. In this paper, we propose to use it to estimate the performance of programs. We first extract a set of paths (either randomly or systematically) from the program, and then obtain a weighted average of the performance of the paths.
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