Group Theoretical Methods in Machine Learning
نویسنده
چکیده
منابع مشابه
Explain the theoretical and practical model of automatic facade design intelligence in the process of implementing the rules and regulations of facade design and drawing
Artificial intelligence has been trying for decades to create systems with human capabilities, including human-like learning; Therefore, the purpose of this study is to discover how to use this field in the process of learning facade design, specifically learning the rules and standards and national regulations related to the design of facades of residential buildings by machine with a machine ...
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The current world is moving towards the development of hardware or software presence of artificial intelligence in all fields of human work, and architecture is no exception. Now this research seeks to present a theoretical and practical model of intuitive design intelligence that shows the problem of learning layout and spatial relationships to artificial intelligence algorithms; Therefore, th...
متن کاملForecasting the Tehran Stock market by Machine Learning Methods using a New Loss Function
Stock market forecasting has attracted so many researchers and investors that many studies have been done in this field. These studies have led to the development of many predictive methods, the most widely used of which are machine learning-based methods. In machine learning-based methods, loss function has a key role in determining the model weights. In this study a new loss function is ...
متن کاملEvaluating machine learning methods and satellite images to estimate combined climatic indices
The reflections recorded on satellite images have been affected by various environmental factors. In these images, some of these factors are combined with other environmental factors that cannot be distinguished. Therefore, it seems wise to model these environmental phenomena in the form of hybrid indicators. In this regard, satellite imagery and machine learning methods can play a unique role ...
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Anti-Friction Bearing (AFB) is a very important machine component and its unscheduled failure leads to cause of malfunction in wide range of rotating machinery which results in unexpected downtime and economic loss. In this paper, ensemble machine learning techniques are demonstrated for the detection of different AFB faults. Initially, statistical features were extracted from temporal vibratio...
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