Proceedings of the 2016 IEEE Eighth International Conference on Intelligent Systems
Increasing amount of scientific publications makes it difficult to conduct a comprehensive review and objectively compare results of previous researches. In some areas of research it is also difficult to extract regularities without computer aid due to complexity of experimental setup and results. Cancer treatment using dendritic cell vaccines is such an area. In this paper we describe a framework for semi-automatic information extraction and further analysis. We also present a case study in the field of dendritic cell vaccination and the corresponding experimental results, which include analysis of separability, classification and regression quality evaluation and cause relations mining.
In this paper a sign-based or semiotic formalism is considered. Neurophysiological and psychological researches indicate sign-based structures, which are the basic elements of the world model of a human subject. These elements are formed during his/her activity and communication. In this formalism it was possible to formulate and solve the problem of goal-setting, i.e. generating the goal of behavior.
In this paper we propose a new machine learning concept called randomized machine learning, in which model parameters are assumed random and data are assumed to contain random errors. Distinction of this approach from "classical" machine learning is that optimal estimation deals with the probability density functions of random parameters and the "worst" probability density of random data errors. As the optimality criterion of estimation, randomized machine learning employs the generalized information entropy maximized on a set described by the system of empirical balances. We apply this approach to text classification and dynamic regression problems. The results illustrate capabilities of the approach.