We address instance-based learning from a perceptual organization standpoint using tensor voting. The goal of instance-based learning is to learn the underlying relationships between observations, which are points in an N -D continuous space, under the assumption that they lie in a limited part of the N -D space, typically a manifold, the dimensionality of which is an indication of the degrees ...

We discuss aspects of generic two-dimensional dilaton gravity theories. The geometry is in general conformal to the anti--de Sitter space (${\mathrm{AdS}}_{2}$) and has IR curvature singularities at zero temperature; this can be regulated by a black hole. on-shell action divergent; we holographic energy-momentum tensor adding appropriate counterterms. For theories obtained dimensional reduction...

Instance segmentation is a challenging task aiming at classifying and segmenting all object instances of specific classes. While two-stage box-based methods achieve top performances in the image domain, they cannot easily extend their superiority into video domain. This because usually deal with features or images cropped from detected bounding boxes without alignment, failing to capture pixel-...

In this paper, the effect of infrared (IR) roasting conditions on Aflatoxins and total counts of two types of Iranian pistachio kernels, long and round, was studied. The optimized roasting conditions, 70 V for round pistachios and 90 V for long pistachios with 10 cm distance from IR source were used. Naturally Aflatoxin-contaminated kernels were supplied and roasted. Microbiological an...

The paper proposes a heuristic instance reduction algorithm as an approach to machine learning and knowledge discovery in centralized and distributed databases. The proposed algorithm is based on an original method for a selection of reference instances and creates a reduced training dataset. The reduced training set consisting of selected instances can be used as an input for the machine learn...

Instance-based learning methods such as the nearest neighbor classifier have proven to perform well in pattern classification in several fields. Despite their high classification accuracy, they suffer from a high storage requirement, computational cost, and sensitivity to noise. In this paper, we present a data reduction method for instance-based learning, based on entropy-based partitioning an...

In instance-based learning, a training set is given to a classifier for classifying new instances. In practice, not all information in the training set is useful for classifiers. Therefore, it is convenient to discard irrelevant instances from the training set. This process is known as instance reduction, which is an important task for classifiers since through this process the time for classif...

Feature selection techniques have been successfully applied in many applications for making supervised learning more effective and efficient. These techniques have been widely used and studied in traditional supervised learning settings, where each instance is expected to have a label. In multiple instance learning (MIL) each example or bag consists of a variable set of instances, and the label...