33 research outputs found
Communication Strategy of Kenvelo
This thesis is dedicated to the problem of communication strategy in the fashion business with application on one of the fashion retailers, Kenvelo. Clothing business, or as we will refer here fashion business is together with food, transportation and communication businesses one of the most natural and essential parts of our lives. Nonetheless, fashion business is one of the most challenging and unpredictable ones, driven by many different factors and sometimes hardly comprehensive links. The main aim of the thesis is to identify the communication tools appropriate for the target group of Kenvelo, develop a new concept for communication and propose a media plan which would support this concept
Řízení kurzového rizika
Práce se zaměřuje na základy problematiky řízení kurzového rizika, měnových trhů a operací s deriváty. Zpracovává základní pojmy pro teoretický i praktický základ znalostí v dané oblasti. Cílem je poskytnout základní, ucelený přehled o možnostech řizení kurzového rizika se zaměřením na praktické využití
Gradient Descent Parameter Learning of Bayesian Networks under Monotonicity Restrictions
Learning parameters of a probabilistic model is a necessary step in most machine learning modeling tasks. When the model is complex and data volume is small the learning process may fail to provide good results. In this paper we present a method to improve learning results for small data sets by using additional information about the modelled system. This additional information is represented by monotonicity conditions which are restrictions on parameters of the model. Monotonicity simplifies the learning process and also these conditions are often required by the user of the system to hold. \n\nIn this paper we present a generalization of the previously used algorithm for parameter learning of Bayesian Networks under monotonicity conditions. This generalization allows both parents and children in the network to have multiple states. The algorithm is described in detail as well as monotonicity conditions are.\n\nThe presented algorithm is tested on two different data sets. Models are trained on differently sized data subsamples with the proposed method and the general EM algorithm. Learned models are then compared by their ability to fit data. We present empirical results showing the benefit of monotonicity conditions. The difference is especially significant when working with small data samples. The proposed method outperforms the EM algorithm for small sets and provides comparable results for larger sets
Question Selection Methods for Adaptive Testing with Bayesian Networks
The performance of Computerized Adaptive Testing systems, which are used for testing of human knowledge, relies heavily on methods selecting correct questions for tested students. In this article we propose three different methods selecting questions with Bayesian networks as students’ models. We present the motivation to use these methods and their mathematical description. Two empirical datasets, paper tests of specific topics in mathematics and Czech language for foreigners, were collected for the purpose of methods’ testing. All three methods were tested using simulated testing procedure and results are compared for individual methods. The comparison is done also with the sequential selection of questions to provide a relation to the classical way of testing. The proposed methods are behaving much better than the sequential selection which verifies the need to use a better selection method. Individually, our methods behave differently, i.e., select different questions but the success rate of model’s predictions is very similar for all of them. This motivates further research in this topic to find an ordering between methods and to find the best method which would provide the best possible selections in computerized adaptive tests