Machine learning deployment for arms dynamics pattern recognition in Southeast Asia region
نویسندگان
چکیده
<p>Finding the most significant determinant variable of arms dynamic is highly required due to strategic policies formulations and power mapping for academics policy makers. Machine learning still new or underdiscussed among study politics international relations. Existing literature have much focus on using advanced quantitative methods by applying various types regression analysis. This analyzed in Southeast Asia countries along with its some partners such as United States, China, Russia, South Korea, Japan ‘Decision Tree’ machine algorithm. conducted a analysis 55 items which classified into 8 classes variables videlicet defense budget, trade exports, imports, political posture, economic security posture priority, national capability, direct contact,. The results suggest three findings: (1) state who perceives maritime drivers forces will seek more translated (2) big size tend be an exporter country, (3) state’s energy dependence often leads higher volume transfers between countries.</p>
منابع مشابه
Machine Learning Pattern Recognition
Introduction These lecture notes were written to provide you with a handy reference to the material that was presented in the Machine Learning: Pattern Recognition course. It is not meant to replace the book of the course [Bishop, 2007], rather, it is meant to illustrate topics that may seem a bit dry or emphasise subjects that are deemed important to this class. It will often repeat what was s...
متن کاملPattern Recognition and Machine Learning
his book provides an introduction to the eld of pattern recognition and machine earning. It gives an overview of several asic and advanced topics in machine earning theory. The book is definitely aluable to scientists and engineers who re involved in developing machine learnng tools applied to signal and image proessing applications. This book is also uitable for courses on machine learning nd ...
متن کاملPattern Recognition and Machine Learning
In this note, we describe the sequential minimal optimization (SMO) algorithm to solve the soft margin support vector machine (SVM) binary classification problem, which is to be implemented as part of the miniproject. In order to understand our arguments here, the reader is advised to study the SVM chapter of the course notes, in particular the “Solving the Support Vector Machine” section (the ...
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: Indonesian Journal of Electrical Engineering and Computer Science
سال: 2021
ISSN: ['2502-4752', '2502-4760']
DOI: https://doi.org/10.11591/ijeecs.v23.i3.pp1654-1662