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Multi-model Approach For Effective Multimedia Exploration
Grošup, Tomáš ; Lokoč, Jakub (advisor) ; Somol, Petr (referee)
This work is focusing on exploration of multimedia collections. It describes the problematic of exploration and proposes new approaches to it, two based on the data structure M-Index and two utilizing multiple similarity models at once. Those approaches were compared using an extensive user study. Part of this work is also devoted to analysis of a new exploration system, design of its architecture, system implementation and its deployment. This exploration system was used in several applications, which are also shown and described in this thesis.
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Aplikace umělých neuronových sítí pro detekci malware v HTTPS komunikaci
Bodnár, Jan ; Lokoč, Jakub (advisor) ; Somol, Petr (referee)
A huge proportion of modern malicious software uses Internet connec- tions. Therefore, it is possible to detect infected computers by inspecting network activity. Since attackers hide the content of communication by com- municating over encrypted protocols such as HTTPS, communication must be analysed purely on the basis of metadata. Cisco provided us a dataset containing aggregated metadata with additional information as to whether or not each sample contains malicious communication. This work trains neu- ral networks to distinguish between infected and benign samples, comparing different architectures of neural networks and providing a comparison with results achieved by different machine learning methods tried by colleagues. It also seeks to create a mapping which maps samples of communication into a space where different samples of malicious communication created by a sin- gle malware family form clusters. This may make it easier to find different computers infected by a virus with known behaviour, even when the virus cannot be detected by the detection system. 1
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Multi-model Approach For Effective Multimedia Exploration
Grošup, Tomáš ; Lokoč, Jakub (advisor) ; Somol, Petr (referee)
This work is focusing on exploration of multimedia collections. It describes the problematic of exploration and proposes new approaches to it, two based on the data structure M-Index and two utilizing multiple similarity models at once. Those approaches were compared using an extensive user study. Part of this work is also devoted to analysis of a new exploration system, design of its architecture, system implementation and its deployment. This exploration system was used in several applications, which are also shown and described in this thesis.
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A Comparison of Adaptive Sampling and Interpolation of 2D BRDF Subspaces
Vávra, Radomír ; Filip, Jiří ; Somol, P.
This report comprises overview of interpolation and sampling methods of Bidirectional Reflectance Distribution Function (BRDF). We analyzed 2D BRDF subspaces of eleven materials. We compared performance of five interpolation methods, three different sampling patterns, and compared twelve adaptive sampling strategies. Finally, based on knowledge of entire data we estimated sub-optimal sampling patterns and as a reference compared them with other tested sampling approaches.
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Fast Dependency-Aware Feature Selection in Very-High-Dimensional Pattern Recognition Problems
Somol, Petr ; Grim, Jiří
The paper addresses the problem of making dependency-aware feature selection feasible in pattern recognition problems of very high dimensionality. The idea of individually best ranking is generalized to evaluate the contextual quality of each feature in a series of randomly generated feature subsets. Each random subset is evaluated by a criterion function of arbitrary choice (permitting functions of high complexity). Eventually, the novel dependency-aware feature rank is computed, expressing the average benefit of including a feature into feature subsets. The method is efficient and generalizes well especially in very-high-dimensional problems, where traditional context-aware feature selection methods fail due to prohibitive computational complexity or to over-fitting. The method is shown well capable of over-performing the commonly applied individual ranking which ignores important contextual information contained in data.
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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.
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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.
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Vyhodnocení stability jednotlivých metod i skupin metod výběru příznaků, který optimalizují kardinalitu podmnožiny příznaků
Somol, Petr ; Novovičová, Jana
Stability (robustness) of feature selection methods is a topic of recent interest yet often neglected importance with direct impact on the reliability of machine learning systems. We investigate the problem of evaluating the stability of feature selection processes yielding subsets of varying size. We introduce several novel feature selection stability measures and adjust some existing measures in a unifying framework that offers broad insight into the stability problem. We study in detail the properties of considered measures and demonstrate on various examples what information about the feature selection process can be gained. We also introduce an alternative approach to feature selection evaluation in form of measures that enable comparing the similarity of two feature selection processes. These measures enable comparing, e.g., the output of two feature selection methods or two runs of one method with different parameters. The information obtained using the considered stability and similarity measures is shown usable for assessing feature selection methods (or criteria) as such
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