Search results for "A* algorithm"
showing 10 items of 2538 documents
LogDet divergence-based metric learning with triplet constraints and its applications.
2014
How to select and weigh features has always been a difficult problem in many image processing and pattern recognition applications. A data-dependent distance measure can address this problem to a certain extent, and therefore an accurate and efficient metric learning becomes necessary. In this paper, we propose a LogDet divergence-based metric learning with triplet constraints (LDMLT) approach, which can learn Mahalanobis distance metric accurately and efficiently. First of all, we demonstrate the good properties of triplet constraints and apply it in LogDet divergence-based metric learning model. Then, to deal with high-dimensional data, we apply a compressed representation method to learn…
A Case Study on Vestibular Sensations in Driving Simulators.
2022
Motion platforms have been used in simulators of all types for several decades. Since it is impossible to reproduce the accelerations of a vehicle without limitations through a physically limited system (platform), it is common to use washout filters and motion cueing algorithms (MCA) to select which accelerations are reproduced and which are not. Despite the time that has passed since their development, most of these algorithms still use the classical washout algorithm. In the use of these MCAs, there is always information that is lost and, if that information is important for the purpose of the simulator (the training simulators), the result obtained by the users of that simulator will no…
Learning-Graph-Based Quantum Algorithm for k-distinctness
2012
We present a quantum algorithm solving the $k$-distinctness problem in $O(n^{1-2^{k-2}/(2^k-1)})$ queries with a bounded error. This improves the previous $O(n^{k/(k+1)})$-query algorithm by Ambainis. The construction uses a modified learning graph approach. Compared to the recent paper by Belovs and Lee arXiv:1108.3022, the algorithm doesn't require any prior information on the input, and the complexity analysis is much simpler. Additionally, we introduce an $O(\sqrt{n}\alpha^{1/6})$ algorithm for the graph collision problem where $\alpha$ is the independence number of the graph.
The Bayesian Learning Automaton — Empirical Evaluation with Two-Armed Bernoulli Bandit Problems
2009
The two-armed Bernoulli bandit (TABB) problem is a classical optimization problem where an agent sequentially pulls one of two arms attached to a gambling machine, with each pull resulting either in a reward or a penalty. The reward probabilities of each arm are unknown, and thus one must balance between exploiting existing knowledge about the arms, and obtaining new information.
Sizing and shape optimization material use in 10 bar trusses
2021
Truss optimization has the goal of achieving savings in costs and material while maintaining structural characteristics. In this research a 10 bar truss was structurally optimized in Rhino 6 using genetic algorithm optimization method. Results from previous research where sizing optimization was limited to using only three different cross-sections were compared to a sizing and shape optimization model which uses only those three cross-sections. Significant savings in mass have been found when using this approach. An analysis was conducted of the necessary bill of materials for these solutions. This research indicates practical effects which optimization can achieve in truss design.
The Geochemistry of Basalt Handaxes from the Lower Palaeolithic Site of Ma‛ayan Baruch, Israel-A Perspective on Raw Material Selection
2014
The Upper Acheulian site of Ma‛ayan Baruch, northern Israel, is primarily known for its exceptionally large assemblage of thousands of flint handaxes. Within this assemblage, a minute collection of basalt handaxes was retrieved as well, representing particular technological choice within the Upper Acheulian. Using geochemistry, we were able to determine that these basalt handaxes were not made from local basalt, but from different sources. Thus, the use of basalt at the site does not represent an ad hoc choice of using local raw material but, rather, a more complex technological choice pertaining to variability in raw material selection in the Lower Palaeolithic Levant.
Some Effects of Individual Learning on the Evolution of Sensors
2001
In this paper, we present an abstract model of sensor evolution, where sensor development is only determined by artificial evolution and the adaptation of agent reactions is accomplished by individual learning. With the environment cast into a MDP framework, sensors can be conceived as a map from environmental states to agent observations and Reinforcement Learning algorithms can be utilised. On the basis of a simple gridworld scenario, we present some results of the interaction between individual learning and evolution of sensors.
Aplicación del Estimador de Parámetros de Segmentación por Media-desplazada (EPSM) a las imágenes de satélite de muy alta resolución espacial: Tetuán…
2015
<p>La segmentación de imágenes constituye un paso crucial en el Análisis de Imágenes Basado en Objetos (AIBO). Combinando distintos valores de los parámetros de entrada de los algoritmos de segmentación se obtienen diferentes resultados. En general, los parámetros óptimos seleccionados se determinan mediante interpretación visual; por lo tanto, la definición de las combinaciones óptimas es una tarea considerablemente difícil. En la presente investigación, se propone una herramienta analítica que denominamos Estimador de Parámetros de Segmentación por Media-desplazada (EPSM) aplicada a la selección automatizada de los valores de los parámetros de segmentación en las imágenes de satélit…
Practical Issues on Energy-Growth Nexus Data and Variable Selection With Bayesian Analysis
2018
Abstract Given that the energy-growth nexus (EGN) is short of a complete theoretical base, the production function used therein is typically complemented with numerous variables that characterize an economy. Researchers are often puzzled not only with the selection of variables per se, but also with the variable sources and the various data handlings which become apparent and available only after years of experience in this research field. Thus, this chapter is divided into two distinctive parts: The first part contains an overview of the available data sources for the EGN as well as a succinct selection of advice on data handlings, transformations, and interpretations that could come handy…
On incorporating the paradigms of discretization and Bayesian estimation to create a new family of pursuit learning automata
2013
Published version of an article in the journal: Applied Intelligence. Also available from the publisher at: http://dx.doi.org/10.1007/s10489-013-0424-x There are currently two fundamental paradigms that have been used to enhance the convergence speed of Learning Automata (LA). The first involves the concept of utilizing the estimates of the reward probabilities, while the second involves discretizing the probability space in which the LA operates. This paper demonstrates how both of these can be simultaneously utilized, and in particular, by using the family of Bayesian estimates that have been proven to have distinct advantages over their maximum likelihood counterparts. The success of LA-…