In this paper, it is assumed that the “Decision Making Units“( ) are consist of positive and negative input and output. Firstly, the optimistic and pessimistic models have been suggested by using negative data and then units with most productive scale size are measured in optimistic and pessimistic models. These productive values are compared with double frontiers and Hurwicz’s Criterion to obt...

The Problem: Learning from data with both labeled training points (x,y pairs) and unlabeled training points (x alone). For the labeled points, supervised learning techniques apply, but they cannot take advantage of the unlabeled points. On the other hand, unsupervised techniques can model the unlabeled data distribution, but do not exploit the labels. Thus, this task falls between traditional s...

Partially observed functional data are frequently encountered in applications and the object of an increasing interest by literature. We here address problem measuring centrality a datum partially sample. propose integrated depth for data, dealing with very challenging case where partial observability can occur systematically on any observation dataset. In particular, differently from many tech...

in this paper, it is assumed that the “decision making units“( ) are consist of positive and negative input and output. firstly, the optimistic and pessimistic models have been suggested by using negative data and then units with most productive scale size are measured in optimistic and pessimistic models. these productive values are compared with double frontiers and hurwicz’s criterion to obt...

One of the most important artificial immune algorithms is negative selection algorithm, which is an anomaly detection and pattern recognition technique; however, recent research has shown the successful application of this algorithm in data classification. Most of the negative selection methods consider deterministic boundaries to distinguish between self and non-self-spaces. In this paper, two...

Belief compression improves the tractability of large-scale partially observable Markov decision processes (POMDPs) by finding projections from high-dimensional belief space onto low-dimensional approximations, where solving to obtain action selection policies requires fewer computations. This paper develops a unified theoretical framework to analyse three existing linear belief compression app...

Previous partially supervised classification methods can partition unlabeled data into positive examples and negative examples for a given class by learning from positive labeled examples and unlabeled examples, but they cannot further group the negative examples into meaningful clusters even if there are many different classes in the negative examples. Here we proposed an automatic method to o...