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This volume comprises proceedings of the 2nd IEEE International Conference on Cybernetics (CYBCONF 2015), organized by Gdynia Maritime University, IEEE Systems, Man, and Cybernetics Society (SMC), IEEE Poland Section, IEEE SMC Technical Committee on Computational Collective Intelligence, in cooperation with Gdynia Maritime University's Students and Alumni Foundation, and Pomeranian Science and Technology...
Clustering is perhaps one of the most popular approaches used in unsupervised machine learning. There's a huge number of different methods and algorithms that have been designed in the last decades related to this “blind pattern search”, some of these approaches are based on bio-inspired methods such as Evolutionary Computation, Swarm Intelligence or Neural Networks among others. In the last years,...
The concept of Decisional DNA is decade old. This article introduces the initial idea of Set of Experience Knowledge Structure, its advancement into Decisional DNA, and its potential for real life applications in divers domains. The most current and future research steps into Industry 4.0 are also presented and discussed
Learning with class imbalanced data sets is a challenging undertaking by the common learning algorithms. These algorithms favor majority class due to imbalanced class representation, noise and their inability to expand the boundaries of minority class in concept space. To improve the performance of minority class identification, ensembles combined with data resampling techniques have gained much popularity...
Classical reinforcement learning techniques are often inadequate for problems with large state-space due to curse of dimensionality. If the states can be represented as a set of variables, it is possible to model the environment more compactly. Automatic detection and use of temporal abstractions during learning was proven to be effective to increase learning speed. In this paper, we propose a factored...
Pattern classification or clustering plays important role in a wide variety of applications in different areas like psychology and other social sciences, biology and medical sciences, pattern recognition and data mining. A lot of algorithms for supervised or unsupervised classification have been developed so far in order to achieve high classification accuracy with lower computational cost. However,...
Modern computer systems generate massive amounts of data in real-time. We have come to the age of big data, where the amount of information exceeds the perceptive abilities of any human being. Frequently the massive data collections arrive over time, in the form of a data stream. Not only the volume and velocity of data poses a challenge for machine learning systems, but also its variability. Such...
Initialization of neuron weights is one of key problems in artificial neural networks (ANNs). This problem is particularly important in ANNs implemented as Application Specific Integrated Circuits (ASICs), where the number of the weights becomes large. When ANNs are implemented in software, the weights can be easily programmed. In contrast, in parallel systems of this type realized as ASICs it is...
In recent years, various aware systems have been developed in the context of ubiquitous computing to improve the quality of services (QoS). The ultimate goal of awareness computing (AC) is to establish a win-win relation between producers and consumers. On the other hand, the main purpose of computational awareness (CA) is to understand the mechanism of awareness in human or animal brains, so that...
Model-based collaborative filtering improves the fundamental limitations of the collaborative filtering facing the issues of data sparsity and scalability while presenting other constraints of high costs of model building and the tradeoff between performance and scalability. Such tradeoff results in reduced coverage, which is one sort of the sparsity issue. Furthermore, high model building costs lead...
The paper describes an application of the dynamic programming method to determine own ship's safe trajectory during the passing of other ships encountered. A dynamic model of the process with kinematic constraints of state determined by a three-layer artificial neural network has been used for the synthesis of control. Non-linear activation functions in the first and second layers may be characterised...
In this article the approximation capability of the extreme learning machine is studied. Specifically the impact of the range from which the input weights and biases are randomly generated on the fitted curve complexity is analyzed. The guidance for how to generate the input weights and biases to get good performance in approximation of the functions of one variable is provided.
The idea presented in this paper is to gradually decrease the influence of selected training vectors on the model: if there is a higher probability that a given vector is an outlier, its influence on training the model should be limited. This approach can be used in two ways: in the input space (e.g. with such methods as k-NN for prediction and for instance selection) and in the output space (e.g...
Numeric experiments on random, relatively large travelling salesman problems presented in this paper show that passive neural networks can be used as an efficient, dynamic optimization tool for combinatorial programming.
The paper addresses the problem of the radial basis function network initialization with feature section carried-out independently for each hidden unit. In each case a unique subset of features is derived from respective clusters of instances using the rotation-based ensembles technique. The process of the RBFN design with cluster-dependent features, including initialization and training, is carried-out...
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