Design Informtion Optimum Vector Machine Learning Systems
نویسنده
چکیده
ABSTRACT: Data driven decision methods generated from analysis or experiments are currently developing a large amount of interest in the machine learning community. It is of interest to develop automated methods to infer from data and ensure judicious decisions are based on information provided by data. Computational efficiency, predictive generalization and statistical efficiency are the main challenges for working with data The primary objective of current work is to develop a methodology to learn from generated data (experimental or analytical) and be able to generalize with high confidence. Data identified as design information vectors are utilized as a basis to learn patterns and identify critical features of the design space.
منابع مشابه
Fault diagnosis in a distillation column using a support vector machine based classifier
Fault diagnosis has always been an essential aspect of control system design. This is necessary due to the growing demand for increased performance and safety of industrial systems is discussed. Support vector machine classifier is a new technique based on statistical learning theory and is designed to reduce structural bias. Support vector machine classification in many applications in v...
متن کاملOnline Voltage Stability Monitoring and Prediction by Using Support Vector Machine Considering Overcurrent Protection for Transmission Lines
In this paper, a novel method is proposed to monitor the power system voltage stability using Support Vector Machine (SVM) by implementing real-time data received from the Wide Area Measurement System (WAMS). In this study, the effects of the protection schemes on the voltage magnitude of the buses are considered while they have not been investigated in previous researches. Considering overcurr...
متن کاملOPTIMAL SHAPE DESIGN OF GRAVITY DAMS BASED ON A HYBRID META-HERURISTIC METHOD AND WEIGHTED LEAST SQUARES SUPPORT VECTOR MACHINE
A hybrid meta-heuristic optimization method is introduced to efficiently find the optimal shape of concrete gravity dams including dam-water-foundation rock interaction subjected to earthquake loading. The hybrid meta-heuristic optimization method is based on a hybrid of gravitational search algorithm (GSA) and particle swarm optimization (PSO), which is called GSA-PSO. The operation of GSA-PSO...
متن کاملMachine Learning Algorithm for Prediction of Heavy Metal Contamination in the Groundwater in the Arak Urban Area
This paper attempts to predict heavy metals (Pb, Zn and Cu) in the groundwater from Arak city, using support vector regression model(SVR) by taking major elements (HCO3, SO4) in the groundwater from Arak city. 150 data samples and several models were trained and tested using collected data to determine the optimum model in which each model involved two inputs and three outputs. This SVR model f...
متن کاملAnomaly Detection Using SVM as Classifier and Decision Tree for Optimizing Feature Vectors
Abstract- With the advancement and development of computer network technologies, the way for intruders has become smoother; therefore, to detect threats and attacks, the importance of intrusion detection systems (IDS) as one of the key elements of security is increasing. One of the challenges of intrusion detection systems is managing of the large amount of network traffic features. Removing un...
متن کامل