Learning Patterns in Noisy Data: The AQ Approach
نویسندگان
چکیده
منابع مشابه
Damage identification of structures using second-order approximation of Neumann series expansion
In this paper, a novel approach proposed for structural damage detection from limited number of sensors using extreme learning machine (ELM). As the number of sensors used to measure modal data is normally limited and usually are less than the number of DOFs in the finite element model, the model reduction approach should be used to match with incomplete measured mode shapes. The second-order a...
متن کاملTexture Recognition through Machine Learning and Concept Optimiziation
This paper justifies and demonstrates a machine learning approach to the problem of texture recognition. The learning-based texture recognition is separated into the following phases: (i) the acquisition of texture concepts, (ii) the optimization of concept prototypes, and (iii) the recognition of unknown texture samples. Methodology adapted to the acquisition and recognition of-noisy texture d...
متن کاملA Multi-Objective Approach to Fuzzy Clustering using ITLBO Algorithm
Data clustering is one of the most important areas of research in data mining and knowledge discovery. Recent research in this area has shown that the best clustering results can be achieved using multi-objective methods. In other words, assuming more than one criterion as objective functions for clustering data can measurably increase the quality of clustering. In this study, a model with two ...
متن کاملتشخیص هوشمند و سریع بیماری قلبی بر اساس همافزایی شبکههای عصبی خطی و روش رگرسیون منطقی
Background and purpose: Diseases have been the greatest threat for human being along the history. Heart disease (HD) has gained special attention in medical studies. Recently studying on classification and diagnosis of HD as a key topic and a lot of researches have been done in order to increase precise and reduce error in this type of decisions. With development of intelligent learning syst...
متن کاملطبقه بندی و شناسایی رخسارههای زمینشناسی با استفاده از دادههای لرزه نگاری و شبکههای عصبی رقابتی
Geological facies interpretation is essential for reservoir studying. The method of classification and identification seismic traces is a powerful approach for geological facies classification and distinction. Use of neural networks as classifiers is increasing in different sciences like seismic. They are computer efficient and ideal for patterns identification. They can simply learn new algori...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2001