|
|
Practical use of knowledge systems in automotive diagnostics
Koláček, Miroslav ; Pudil, Pavel (advisor) ; Novák, Michal (referee)
This thesis in its theoretical part summarizes international knowledge of the problematics of expert systems and their application in automotive diagnostics. In the practical part it solves the methodology and selection of appropriate expert system for the development of diagnostic application. This issue is further elaborated by the methodology of an expert´s knowledge transfer into the knowledge database and creating application for its use within the Windows, Web and Android platforms. Beside the working out the product itself it also provides instructions for others who are interested in preparing similar applications and topic for the future development of the already prepared tool.
|
|
|
Use of Methods of Managerial Decision-Making in Introducing a new Product in the Market of Security Services
Brož, Jan ; Pudil, Pavel (advisor) ; Bína, Vladislav (referee)
The aim of the thesis is to evaluate the suitability of methods of managerial decision-making in practice. The thesis highlights the diversity of selected methods, particularly in relation to the use of discrete and continuous risk factors. The methods are applied to the case of the introduction of new product in the market of commercial security services. The thesis includes a complete strategic planning cycle based on the defined objectives of the organization, security agency SECURE. From the analytical part, including the analysis of the competitive environment 5F, through the creation of strategies, author proceeds to an implementation part which is the essence of the thesis. It is represented by managerial decision-making under risk and uncertainty, particularly decision matrix and the method of Monte Carlo. The conclusion contains evaluation and comparison of different methods and their contribution to practical use.
|
|
|
Use of Methods of Managerial Decision-Making in foundation of the new enterprise on the market
Oberhel, Martin ; Pudil, Pavel (advisor) ; Bína, Vladislav (referee)
This thesis is focused on the methods and tools which are helpful in decision-making under uncertainty and risk. The methods of decision-making for discrete and continuous values of risk factors are used in the thesis. In case of discrete values of risk factors and decision-making under risk, the thesis uses the rule of expected values, the rule of expected value and variance and also calculates the value of perfect information. In case of decision-making under uncertainty, the thesis is focused on the rule of maximin and maximax, Laplace's rule, Hurwitz's rule and Savage's rule. The following part of the thesis is devoted to decision-making with continuous values of risk factors. It utilizes the Monte Carlo simulation method and the sensitivity analysis with the help of Lumina Analytica software. The last part of the thesis is aimed at utilization of decision trees in case of multistage decision-making. It uses the Treeplan software which works as a plugin in MS office Excel. All the mentioned methods are practically applied to a concrete case of analysing and ex post evaluating the business plans of a company, which is based at Jindřichův Hradec market.
|
|
| |
|
| |
|
|
Introduction to Feature Selection Toolbox 3 – The C++ Library for Subset Search, Data Modeling and Classification
Somol, Petr ; Vácha, Pavel ; Mikeš, Stanislav ; Hora, Jan ; Pudil, Pavel ; Žid, Pavel
We introduce a new standalone widely applicable software library for feature selection (also known as attribute or variable selection), capable of reducing problem dimensionality to maximize the accuracy of data models, performance of automatic decision rules as well as to reduce data acquisition cost. The library can be exploited by users in research as well as in industry. Less experienced users can experiment with different provided methods and their application to real-life problems, experts can implement their own criteria or search schemes taking advantage of the toolbox framework. In this paper we first provide a concise survey of a variety of existing feature selection approaches. Then we focus on a selected group of methods of good general performance as well as on tools surpassing the limits of existing libraries. We build a feature selection framework around them and design an object-based generic software library. We describe the key design points and properties of the library.
|
|
|
Sequential Retreating Search Methods in Feature Selection
Somol, Petr ; Pudil, Pavel
Inspired by Floating Search, our new pair of methods, the Sequential Forward Retreating Search (SFRS) and Sequential Backward Retreating Search (SBRS) is exceptionally suitable for Wrapper based feature selection. (Conversely, it cannot be used with monotonic criteria.) Unlike most of other known sub-optimal search methods, both the SFRS and SBRS are parameter-free deterministic sequential procedures that incorporate in the optimization process both the search for the best subset and the determination of the best subset size. The subset yielded by either of the two new methods is to be expected closer to optimum than the best of all subsets yielded in one run of the Floating Search. Retreating Search time complexity is to be expected slightly worse but in the same order of magnitude as that of the Floating Search. In addition to introducing the new methods we provide a testing framework to evaluate them with respect to other existing tools.
|
|
|
Výběr nejinformativnějších proměnných ve statistickém rozpoznávání
Pudil, Pavel ; Somol, Petr ; Haindl, Michal
The research report gives an overview of feature selection techniques in statistical pattern recognition with particular emphasis to methods developed by the researchers participating in MATEO Centre of Mechatronics project. Besides discussing the advances in methodology it attempts to put them into a taxonomical framework. The methods discussed include the latest variants of the optimal algorithms, enhanced sub-optimal techniques and the simultaneous semi-parametric probability density function modelling and feature space selection method. Some related issues are illustrated on real data by means of the Feature Selection Toolbox software.
|
|
| |
|
|
Úvod do statistického rozpoznávání
Pudil, Pavel ; Somol, Petr ; Haindl, Michal
Pattern recognition problem is briefly characterized as a process of machine learning. Its main stages (dimensionality reduction and classifier design) are stated. Statistical approach is given priority here. Two approaches to dimensionality reduction, namely feature selection (FS) and feature extraction (FE) are specified. Though FS is a special case of FE, they are very different from a practical viewpoint and thus must be considered separately.
|
guest ::
RSS feed.