A number of image-processing problems can be formulated as optimization problems. The objective function typically contains several terms specifically designed for different purposes. Parameters in front of these terms are used to control the relative weights among them. It is of critical importance to tune these parameters, as quality of the solution depends on their values. Tuning parameter i...

This study evaluates and compares the performance of four machine learning classifiers—support vector machine (SVM), normal Bayes (NB), classification and regression tree (CART) and K nearest neighbor (KNN)—to classify very high resolution images, using an object-based classification procedure. In particular, we investigated how tuning parameters affect the classification accuracy with differen...

I n this chapter we discuss the notion of Evolutionary Algorithm (EA) parameters and propose a distinction between EAs and EA instances, based on the type of parameters used to specify their details. Furthermore, we consider the most important aspects of the parameter tuning problem and give an overview of existing parameter tuning methods. Finally, we elaborate on the methodological issues inv...

This study evaluates and compares the performance of four machine learning classifiers—support vector machine (SVM), normal Bayes (NB), classification and regression tree (CART) and K nearest neighbour (KNN) —to classify very high resolution images, using an object-based classification procedure. In particular, we investigated how tuning parameters affect the classification accuracy with differ...

By using the particle swarm optimization (PSO) algorithm, a novel design method for the self-tuning PID control in a slider–crank mechanism system is presented in this paper. This paper demonstrates, in detail, how to employ the PSO so as to search efficiently for the optimal PID controller parameters within a mechanism system. The proposed approach has superior features, including: easy implem...

In this paper, a novel design method for self-tuning PID controller of in mechanisms system using the particle swarm optimization (PSO) algorithm is presented. This paper demonstrated in detail how to employ the PSO to search efficiently the optimal PID controller parameters of in mechanisms system. The proposed approach had superior features, including easy implementation, stable convergence c...

Evolutionary algorithms (EAs) form a rich class of stochastic search methods that use the Darwinian principles of variation and selection to incrementally improve a set of candidate solutions (Eiben and Smith, 2003; Jong, 2006). Both principles can be implemented from a wide variety of components and operators, many with parameters that need to be tuned if the EA is to perform as intended. Tuni...

Many important business applications use complex database management systems (DBMS). These DBMS have to be administrated and optimized for an optimal performance, especially in time-critical applications. Administration and optimization are very complex and costly tasks. Therefore, researchers and DBMS vendors focus on development of self-tuning techniques for a continuous adaption, e.g., the C...

Lasso is a popular method for high-dimensional variable selection, but it hinges on a tuning parameter that is difficult to calibrate in practice. In this study, we introduce TREX, an alternative to Lasso with an inherent calibration to all aspects of the model. This adaptation to the entire model renders TREX an estimator that does not require any calibration of tuning parameters. We show that...

A new electromechanical coupling model was built to quantitatively analyze the tuning fork probes, especially the complex ones. A special feature of a novel, soft tuning fork probe, that the second eigenfrequency of the probe was insensitive to the effective force gradient, was found and used in a homemade bimodal atomic force microscopy to measure power dissipation quantitatively. By transform...