منابع مشابه
A comparative study of sample selection methods for classification
Sampling of large datasets for data mining is important for at least two reasons. The processing of large amounts of data results in increased computational complexity. The cost of this additional complexity may not be justifiable. On the other hand, the use of small samples results in fast and efficient computation for data mining algorithms. Statistical methods for obtaining sufficient sample...
متن کاملMulti-label Classification: A Comparative Study on Threshold Selection Methods
Dealing with multiple labels is a supervised learning problem of increasing importance. However, in some tasks, certain learning algorithms produce a confidence score vector for each label that needs to be classified as relevant or irrelevant. More importantly, multi-label models are learnt in training conditions called operating conditions, which most likely change in other contexts. In this w...
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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 اولA Comparative Study of SVM and RF Methods for Classification of Alteration Zones Using Remotely Sensed Data
Identification and mapping of the significant alterations are the main objectives of the exploration geochemical surveys. The field study is time-consuming and costly to produce the classified maps. Therefore, the processing of remotely sensed data, which provide timely and multi-band (multi-layer) data, can be substituted for the field study. In this study, the ASTER imagery is used for altera...
متن کاملImprovements in Sample Selection Methods for Image Classification
Traditional image classification algorithms are mainly divided into unsupervised and supervised paradigms. In the first paradigm, algorithms are designed to automatically estimate the classes’ distributions in the feature space. The second paradigm depends on the knowledge of a domain expert to identify representative examples from the image to be used for estimating the classification model. R...
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ژورنال
عنوان ژورنال: Revue Africaine de la Recherche en Informatique et Mathématiques Appliquées
سال: 2007
ISSN: 1638-5713
DOI: 10.46298/arima.1880