This paper deals with optimal learning and provides a uniied viewpoint of most signiicant results in the eld. The focus is on the problem of local minima in the cost function that is likely to aaect more or less any learning algorithm. We give some intriguing links between optimal learning and the computational complexity of loading problems. We exhibit a computational model such that the solut...

Hierarchically exchangeable data are characterized by the exchangeability of a population of units and the exchangeability of observations from each individual unit. A flexible model for such data is the hierarchical logistic-normal model, which provides unconstrained sampling distributions at the within-unit level and an unconstrained covariance structure at the betweenunit level. Also, the sa...

a field experiment was conducted to study the spatial relationships between weed seed bank and population and their distribution models in sugar beet crop (beta vulgaris) in 2009 at motahari  agricultural research station of karaj. sampling from seed bank before sugar beet drilling and weed population in three stages during the growing season were done using square (50*50 cm) and rectangle (25*...

Hierarchically exchangeable data are characterized by the exchangeability of a population of units and the exchangeability of observations from each individual unit. A flexible model for such data is the hierarchical logistic-normal model, which provides unconstrained sampling distributions at the within-unit level and an unconstrained covariance structure at the betweenunit level. Also, the sa...

This paper introduces and addresses a wide class of stochastic bandit problems where the function mapping the arm to the corresponding reward exhibits some known structural properties. Most existing structures (e.g. linear, Lipschitz, unimodal, combinatorial, dueling, . . . ) are covered by our framework. We derive an asymptotic instance-specific regret lower bound for these problems, and devel...

We consider multiarmed bandit problems where the expected reward is unimodal over partially ordered arms. In particular, the arms may belong to a continuous interval or correspond to vertices in a graph, where the graph structure represents similarity in rewards. The unimodality assumption has an important advantage: we can determine if a given arm is optimal by sampling the possible directions...

Self-assembled, irregular ensembles of hemispherical Ga nanoparticles (NPs) were deposited on sapphire by molecular beam epitaxy. These samples, whose constituent unimodal or bimodal distribution of NP sizes was controlled by deposition time, exhibited localized surface plasmon resonances tunable from the ultraviolet to the visible (UV/ vis) spectral range. The optical response of each sample w...

Systems that use unimodal biometrics tend to have less accuracy, variations that are due to intra class, restricted degree of freedom, non-universality, error rates that are not acceptable, etc. Based on these reasons, in the near past, most of the researchers have been concentrating on multimodal biometrics. Multimodal biometrics integrates various types of biometrics which outperform unimodal...

There is a lack of a representative set of test problems for comparing global optimization methods. To remedy this a classiication of essentially un-constrained global optimization problems into unimodal, easy, moderately diicult, and diicult problems is proposed. The problem features giving this classiication are the chance to miss the basin of the global minimum, the dispersion of minima, and...

Unimodal, concave relationships between average network productivity and accumulation or density aggregated across spatially compact regions of urban networks—so called Macroscopic Fundamental Diagrams (MFDs)—have recently been shown to exist on homogeneous street networks. When present, MFD facilitate the modeling traffic congestion at a regional level have led development various control stra...