In this technological age, vast amounts of data are generated. Various statistical methods are used to find patterns in data, including clustering. Many common methods for cluster analysis, such as k-means and Nonnegative Matrix Factorization, require input of the number of clusters in the data. However, usually that number is unknown. There exists a method that uses eigenvalues to compute the ...

Recent researches reveal that deep neural networks are sensitive to label noises hence leading poor generalization performance in some tasks. Although different robust loss functions have been proposed remedy this issue, they suffer from an underfitting problem, thus not sufficient learn accurate models. On the other hand, commonly used Cross Entropy (CE) loss, which shows high standard supervi...

The article suggests an algorithm for regular classifier ensemble methodology. The proposed methodology is based on possibilistic aggregation to classify samples. The argued method optimizes an objective function that combines environment recognition, multi-criteria aggregation term and a learning term. The optimization aims at learning backgrounds as solid clusters in subspaces of the high...

Although generalization and discrimination are commonly used together in machine learning, little has been understood about how these two methods are intrinsically related. This paper describes the idea of complementary discrimination, which exploits semantically the syntactic duality between the two approaches: discriminating a concept is equivalent to generalizing the complement of the concep...

IV-N yes RG 715 I 90 min -19 (up to 5 hours) of selected objects through the available Ha and S I1 interference filters which have a useful field of about 2°. The approximate limiting magnitudes for direct plates are quoted in the table. They depend on the seeing; the numbers given are for medium seeing and deeper plates are obtained under excellent conditions. These values may possibly be furt...

Efficient resource scheduling is essential for maximal utilization of expensive deep learning (DL) clusters. Existing cluster schedulers either are agnostic to machine (ML) workload characteristics, or use heuristics based on operators' understanding particular ML framework and workload, which less efficient not general enough. In this article, we show that DL techniques can be adopted design a...

The article suggests an algorithm for regular classifier ensemble methodology. The proposed methodology is based on possibilistic aggregation to classify samples. The argued method optimizes an objective function that combines environment recognition, multi-criteria aggregation term and a learning term. The optimization aims at learning backgrounds as solid clusters in subspaces of the high...

Assigning a set of objects to groups such that objects in one group or cluster are more similar to each other than the other clusters’ objects is the main task of clustering analysis. SSPCO optimization algorithm is anew optimization algorithm that is inspired by the behavior of a type of bird called see-see partridge. One of the things that smart algorithms are applied to solve is the problem ...