Abstract One-class classification (OCC) is a fundamental problem in pattern recognition with wide range of applications. This work presents semi-supervised quantum machine learning algorithm for such problem, which we call variational one-class classifier (VQOCC). The suitable noisy intermediate-scale computing because the VQOCC trains fully-parameterized autoencoder normal dataset and does not...

Along with the aggressive growing of the amount of digital data available (text, audio samples, digital photos and digital movies joined all in the multimedia domain) the need for classification, recognition and retrieval of this kind of data became very important. In this paper will be presented a system structure to handle multimedia data based on a recognition perspective. The main processin...

We present a new approach to the classification problem for instances with discrete features, based on Bayesian statistics and explicit assumptions about the smoothness of the data. Combined with an optimizing method based on tailored genetic algorithm, our classifier yields performance which rivals that of the best existing methods for several problems like prepositional phrase attachment disa...

Nearest neighbor classification expects the class conditional probabilities to be locally constant. The assumption becomes invalid in high dimension due to the curse−of−dimensionality. Severe bias can be introduced under this condition when using nearest neighbor rule. We propose an adaptive nearest neighbor classification method ¡°indecisive classifier¡± to minimize bias and variance by avoidi...

the problem of automatic signature recognition has received little attention incomparison with the problem of signature verification, despite its potentialapplications for many business processes and can be used effectively in paperlessoffice projects. this paper presents model-based off-line signature recognition withrotation invariant features. non-linear rotation of signature patterns is one...

Artificial Intelligence (AI) methods are used to build classifiers that give different levels of accuracy and solution explication. The intent of this paper is to provide a way of building a hierarchical classifier composed of several artificial neural networks (ANN’s) organised in a tree-like fashion. Such method of construction allows for partition of the original problem into several sub-pro...

Supervised Machine Learning is an important eld with many immediate applications. As a result, there is an increasing number of public domain tools with a diversity of learning approaches. However, very little work has been done to identify which public domain machine learning tools are \best" and on what kind of data. This research is a comparative study of di erent supervised public domainMac...

The common approach for constructing a classifier particle selection assumes reasonable consistency between train data samples and the target sample used particular analysis. However, may have very different properties, like energy spectra signal background contributions. We propose new method based on an ensemble of pre-trained classifiers, each trained exclusive subset, basket, total dataset....