Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userLecture4 (V1) : plot in R - Dr. Eng Fadi CHAABAN
محاضرة4 (V1) : الرسم البياني في R - د. م. فادي شعبان
1193
0 14
0.0
(
0
)
Added by
Tishreen University محاضرة
Publication date
2020
fields
Mathematical Statistics
and research's language is
العربية
Created by
Fadi CHAABAN
Ask ChatGPT about the research
plot من أكثر توابع الرسم استخداماً في R وهو تابع عام Generic Function، أي أنه يمتلك العديد من الطرائق حتى يلائم الكائنات الممررة له.
No English abstract
References used
No references
rate research
Read More
integrate Lists Data Frames
integrate Lists Data Frames
integrate Lists Data Frames
integrate Lists Data Frames
سلسلة محاضرات في لغة البرمجة الإحصائية R
مقدمة عن لغة R - الأوامر الخاصة في لغة البرمجة
Rstudio & R و تطبيقات عملية
R-Studio
الاوامر الخاصة في لغة البرمجة R
ايعازات عامة
الكائنات Objects –
الأشعة Vectors
المصفوفات Matrices
In this scientific paper we dealt with three different types of
homomorphisms between two given ideals in a ring with unity shown as
follows: ring homomorphism, R- module homomorphism and ideal
homomorphism, which were supported by several example
s.
Furthermore, we prove that the family of ideals in a ring R with ring, R -
module and ideal homomorphisms forms the category of ideals of the first,
second and third type, respectively. The next step was dedicated to support all
previous ideals by examples and functor between such categories.
This paper shows that CIDEr-D, a traditional evaluation metric for image description, does not work properly on datasets where the number of words in the sentence is significantly greater than those in the MS COCO Captions dataset. We also show that
CIDEr-D has performance hampered by the lack of multiple reference sentences and high variance of sentence length. To bypass this problem, we introduce CIDEr-R, which improves CIDEr-D, making it more flexible in dealing with datasets with high sentence length variance. We demonstrate that CIDEr-R is more accurate and closer to human judgment than CIDEr-D; CIDEr-R is more robust regarding the number of available references. Our results reveal that using Self-Critical Sequence Training to optimize CIDEr-R generates descriptive captions. In contrast, when CIDEr-D is optimized, the generated captions' length tends to be similar to the reference length. However, the models also repeat several times the same word to increase the sentence length.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
