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Both Semi-Supervised Leaning and Active Learning are techniques used when unlabeled data is abundant, but the process of labeling them is expensive and/or time consuming. In this paper, those two machine learning techniques are combined into a single nature-inspired method. It features particles walking on a network built from the data set, using a unique random-greedy rule to select neighbors to...
Since their early development, genetic programming-based algorithms have been showing to be successful at challenging problems, attaining several human-competitive results and other awards. This paper will present another achievement of such algorithms by describing how our team has won an international machine-learning competition. We have solved, by means of grammar-based genetic programming techniques,...
Traditional data classification considers only physical features (e.g., geometrical or statistical features) of the input data. Here, it is referred to low level classification. In contrast, the human (animal) brain performs both low and high orders of learning and it has facility in identifying patterns according to the semantic meaning of the input data. Data classification that considers not only...
Differently from traditional machine learning techniques applied to data classification, high level classification considers not only the physical features of the data (distance, similarity or distribution), but also the pattern formation of the data. In this latter case, a set of complex network measures are employed because of their abilities to capture spatial, functional and topological relations...
The malicious code analysis allows malware behavior characteristics to be identified, in other words how does it act in the operating system, what obfuscation techniques are used, which execution flows lead to the primary planned behavior, use of network operations, files downloading operations, user and system's information capture, access to records, among other activities, in order to learn how...
The problem of learning finite-state machines (FSM) is tackled by three Ant Colony Optimization (ACO) algorithms. The first two classical ACO algorithms are based on the classical ACO combinatorial problem reduction, where nodes of the ACO construction graph represent solution components, while full solutions are built by the ants in the process of foraging. The third recently introduced mutation-based...
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