Distributions of Matching Distances in Topological Data Analysis
نویسندگان
چکیده
منابع مشابه
analysis of power in the network society
اندیشمندان و صاحب نظران علوم اجتماعی بر این باورند که مرحله تازه ای در تاریخ جوامع بشری اغاز شده است. ویژگیهای این جامعه نو را می توان پدیده هایی از جمله اقتصاد اطلاعاتی جهانی ، هندسه متغیر شبکه ای، فرهنگ مجاز واقعی ، توسعه حیرت انگیز فناوری های دیجیتال، خدمات پیوسته و نیز فشردگی زمان و مکان برشمرد. از سوی دیگر قدرت به عنوان موضوع اصلی علم سیاست جایگاه مهمی در روابط انسانی دارد، قدرت و بازتولید...
15 صفحه اولFour Results in Matching Data Distributions
In Machine Learning, systems are trained with data that is assumed to have the same distribution as the data that will be used for testing later on. In Recommender Systems and other application domains, this assumption does not hold, as the time component of data, which can be seen in changes in fashion, trends, opinion, etc., alters the distribution of the data. Other effects like sampling bia...
متن کاملthe stady and analysis of rice agroclimatology in lenjan
the west of esfahan province, iran, is one of the most important agricultural areas throughout the country due to the climate variability and life-giving water of zayanderood river. rice is one of the major and economic crops in this area. the most important climatic elements in agricultural activities which should be considered include temperature, relative humidity, precipitation and wind. so...
15 صفحه اولTopological Indices Based on Topological Distances in Molecular Graphs
Three new distance—based topological indices are described; two of them, D and D1 (mean distance topological indices, for any graphs, and for acyclic graphs, respectively) have a modest discriminating ability but may be useful for correlations, e. g. with octane numbers. The third index, J (average distance sum connectivity) is the least degenerate single topological index proposed till now. Th...
متن کاملTopological Distances Between Brain Networks
Introduction Many existing brain network distances are based on matrix norms. The element-wise differences may fail to capture underlying topological differences. Further, matrix norms are sensitive to outliers. A few extreme edge weights may severely affect the distance. There is a need to develop network distances that recognize topology. We introduce Gromov-Hausdorff (GH) and KolmogorovSmirn...
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: SIAM Undergraduate Research Online
سال: 2020
ISSN: 2327-7807
DOI: 10.1137/18s017302