Semi-Automatization of Support Vector Machines to Map Lithium (Li) Bearing Pegmatites
نویسندگان
چکیده
منابع مشابه
Semi-Supervised Support Vector Machines
We introduce a semi-supervised support vector machine (S3yM) method. Given a training set of labeled data and a working set of unlabeled data, S3YM constructs a support vector machine using both the training and working sets. We use S3YM to solve the transduction problem using overall risk minimization (ORM) posed by Yapnik. The transduction problem is to estimate the value of a classification ...
متن کاملSemi - supervised Learning in Support Vector Machines
In traditional supervised classification, classifiers are trained using feature/label pairs and the classifier performance is measured on unseen test data. In the current Internet age, as the amount of data produced grows exponentially, we would like to use as much of this data as possible to train classifiers in order to get better performance. This is especially true in models such as neural ...
متن کاملSTAGE-DISCHARGE MODELING USING SUPPORT VECTOR MACHINES
Establishment of rating curves are often required by the hydrologists for flow estimates in the streams, rivers etc. Measurement of discharge in a river is a time-consuming, expensive, and difficult process and the conventional approach of regression analysis of stage-discharge relation does not provide encouraging results especially during the floods. P
متن کاملintroduction to the geology and mineralogy of pegmatites located south of mashhad (first report of li-bearing pegmatites from iran)
the granites south of mashhad are located in the north-east of iran and occur in the suture zone between the binalud and kopeh dagh areas. these s-type granites are peraluminous with an upper triassic age and include intersecting pegmatites. these pegmatites are homogenous and show simple mineralogical combinations which include k-feldspars (microcline and orthoclase), plagioclase, quartz, mica...
متن کاملModel Based Bearing Fault Detection Using Support Vector Machines
This paper deals with the development of a model based method for bearing fault diagnostics. This method effectively combines the information available in the data and the model for efficient classification of the bearing and the type of defect. A four degrees of freedom nonlinear rigid rotor model is used to simulate the rotor bearing system. Precession of the shaft is measured using proximity...
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: Remote Sensing
سال: 2020
ISSN: 2072-4292
DOI: 10.3390/rs12142319