On the consistency of Multithreshold Entropy Linear Classifier

Multithreshold Entropy Linear Classifier (MELC) is a recent classifier idea which employs information theoretic concept in order to create a multithreshold maximum margin model. In this paper we analyze its consistency over multithreshold linear models and show that its objective function upper bounds the amount of misclassified points in a similar manner like hinge loss does in support vector machines. For further confirmation we also conduct some numerical experiments on five datasets.Comment: Presented at Theoretical Foundations of Machine Learning 2015 (http://tfml.gmum.net), final version published in Schedae Informaticae Journa

Experiments on Synchronizing Automata

This work is motivated by the ˇCern´y Conjecture – an old unsolved problem in the automata theory. We describe the results of the experiments on synchronizing automata, which have led us to two interesting results. The first one is that the size of an automaton alphabet may play an important role in the issue of synchronization: we have found a 5-state automaton over a 3-letter alphabet which attains the upper bound from the ˇCern´y Conjecture, while there is no such automaton (except ˇCern´y automaton C5) over a binary alphabet. The second result emerging from the experiments is a theorem describing the dependencies between the automaton structure S expressed in terms of the so-called merging system and the maximal length of all minimal synchronizing words for automata of type S

Fast optimization of Multithreshold Entropy Linear Classifier

Multithreshold Entropy Linear Classifier (MELC) is a density based model which searches for a linear projection maximizing the Cauchy-Schwarz Divergence of dataset kernel density estimation. Despite its good empirical results, one of its drawbacks is the optimization speed. In this paper we analyze how one can speed it up through solving an approximate problem. We analyze two methods, both similar to the approximate solutions of the Kernel Density Estimation querying and provide adaptive schemes for selecting a crucial parameters based on user-specified acceptable error. Furthermore we show how one can exploit well known conjugate gradients and L-BFGS optimizers despite the fact that the original optimization problem should be solved on the sphere. All above methods and modifications are tested on 10 real life datasets from UCI repository to confirm their practical usability.Comment: Presented at Theoretical Foundations of Machine Learning 2015 (http://tfml.gmum.net), final version published in Schedae Informaticae Journa

Java based transistor level CPU simulation speedup techniques

Transistor level simulation of the CPU, while very accurate, brings also the performance challenge. MOS6502 CPU simulation algorithm is analysed with several optimisation techniques proposed. Application of these techniques improved the transistor level simulation speed by a factor of 3–4, bringing it to the levels on par with fastest RTL-level simulations so far

Impact of Clustering Parameters on the Efficiency of the Knowledge Mining Process in Rule-based Knowledge Bases

In this work the subject of the application of clustering as a knowledge extraction method from real-world data is discussed. The authors analyze an influence of different clustering parameters on the quality of the created structure of rules clusters and the efficiency of the knowledge mining process for rules / rules clusters. The goal of the experiments was to measure the impact of clustering parameters on the efficiency of the knowledge mining process in rulebased knowledge bases denoted by the size of the created clusters or the size of the representatives. Some parameters guarantee to produce shorter/longer representatives of the created rules clusters as well as smaller/greater clusters sizes

An improvement in fuzzy entropy edge detection for X-ray imaging

The following paper discusses the topic of edge detection in X-ray hand images. It criticises the existing solution by highlighting a design fault, which is a carelessly chosen function and then proposes a way to eliminate the fault by replacing it with a better suited function. The search for this function and its results are also discussed in this paper. It also presents the aspect of pre- and postprocessing through filtering as another improvement in edge detection

Approaching automatic cyberbullying detection for Polish tweets

This paper presents contribution to PolEval 20191 automatic cyberbullying detection task. The goal of the task is to classify tweets as harmful or normal. Firstly, the data is preprocessed. Then two classifiers adjusted to the problem are tested: Flair and fastText. Flair utilizes character-based language models, which are evaluated using perplexity. Both classifiers obtained similar scores on test data