Visualizations of binary data: A comparative evaluation
نویسندگان
چکیده
منابع مشابه
Visualizations of binary data: A comparative evaluation
Data visualization has the potential to assist humans in analysing and comprehending large volumes of data, and to detect patterns, clusters and outliers that are not obvious using nongraphical forms of presentation. For this reason, data visualizations have an important role to play in a diverse range of applied problems, including data exploration and mining, information retrieval, and intell...
متن کاملComparative Evaluation of Binary Features
Performance evaluation of salient features has a long-standing tradition in computer vision. In this paper, we fill the gap of evaluation for the recent wave of binary feature descriptors, which aim to provide robustness while achieving high computational efficiency. We use established metrics to embed our assessment into the body of existing evaluations, allowing us to provide a novel taxonomy...
متن کاملtransference of imagery: a comparative formalistic study of shakespeares hamlet and its two persian translations
هدف از این تحقیق بررسی انتقال صور خیال هملت در دو ترجمه ی فارسی آن از نظر فرمالیستی بود. برای بدست آوردن داده-های مورد نیاز، 130 نمونه استعاره، مجاز، ایهام، کنایه و پارادوکس در متن اصلی مشخص شده و سپس بر اساس مدل نیومارک (1998) برای ترجمه ی استعاره یا بطور کلی زبان مجاز با معادل های فارسی شان مقایسه گردیدند. این تحقیق بر آن بود تا روش های استفاده شده برای ترجمه هر کدام از انواع زبان مجاز ذکر شد...
15 صفحه اولEvaluation of Serial Periodic, Multi-Variable Data Visualizations
In this paper, I present the results of an evaluation of the effectiveness of a new technique for the visualization and exploration of serial periodic data. At this time, the only other visualization to support this task is the “Spiral” by Carlis and Konstan [1], which an issue with space usage that I attempt to address– namely, the data points on the fringes of the spiral are sparse and the da...
متن کاملBinary Regression With a Misclassified Response Variable in Diabetes Data
Objectives: The categorical data analysis is very important in statistics and medical sciences. When the binary response variable is misclassified, the results of fitting the model will be biased in estimating adjusted odds ratios. The present study aimed to use a method to detect and correct misclassification error in the response variable of Type 2 Diabetes Mellitus (T2DM), applying binary ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Human-Computer Studies
سال: 2003
ISSN: 1071-5819
DOI: 10.1016/s1071-5819(03)00082-x