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This paper presents an automatic clustering system, built as a committee machine, which is used to cohesively partition the self-organizing map. In the proposed method, each expert from the committee machine analyzes the connections of the neuron grid based on a particular similarity matrix, and thus decides which ones should be pruned by gradually removing them and observing the intervals of stability...
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...
This paper explores, with numerical case studies, the performance of an optimization algorithm that is a variant of EPSO, the Evolutionary Particle Swarm Optimization method. EPSO is already a hybrid approach that may be seen as a PSO with self-adaptive weights or an Evolutionary Programming approach with a self-adaptive recombination operator. The new hybrid DEEPSO retains the self-adaptive properties...
This paper provides a brief description on how continuous algorithms can be applied to binary problems. Differential Evolution is the continuous algorithm studied and two versions of this algorithm are presented: the Binary Differential Evolution with a binary encoding and the Discretized Differential Evolution with a continuous encoding. Several discretization methods are presented and the most used...
Network-based Semi-Supervised Learning (NbSSL) propagates labels in affinity-networks by taking advantage of the network topology likewise information spreading in trust networks. In NbSSL, not only the unlabeled data instances, but also the labeled ones, are able to bias the classification performance. Herein, we show some results and discussion on this phenomenon. Even the suitability of the free...
This paper presents a Copula-based Estimation of Distribution Algorithm with Parameter Updating for numeric optimization problems. This model implements an estimation of distribution algorithm using a multivariate extension of the Archimedean copula (MEC-EDA) to estimate the conditional probability for generating a population of individuals. Moreover, the model uses traditional crossover and elitism...
The shortest common superstring problem has important applications in computational biology (e.g. genome assembly) and data compression. This problem is NP-hard, but several heuristic algorithms proved to be efficient for this problem. For example, for the algorithm known as GREEDY it was shown that, if the optimal superstring has the length of N, it produces an answer with length not exceeding 3...
Models of spiking neural networks have a great potential to become a crucial tool in the development of complex network theory. Of particular interest, these models can be used to better understand the important class of brain functional networks, which are frequently studied in the context of computational network analysis. A fundamental question is whether functional connectivity sampling via surface...
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,...
Tuberculosis is an infectious disease widely present in developing countries, which is largely motivated by the difficulty of a rapid and efficient diagnosis. In order to reduce the number of patients suspected of having TB unnecessarily isolated in hospitals, thus optimize the use of health resources, we propose a systematic procedure for developing a decision support system based on specialized...
This paper utilises Evolved Linker Gene Expression Programming (EL-GEP), a new variant of Gene Expression Programming (GEP), to solve symbolic regression and sequence induction problems. The new technique was first proposed in [6] to evolve modularity in robotic behaviours. The technique extends the GEP algorithm by incorporating a new alphabetic set (linking set) from which genome linking functions...
Within functional verification of digital systems there are dynamic methods based on Device Under Verification simulation. We focus on this type of method using functional coverage points. Nowadays, the main problem consists in obtaining high values to exercise all functional coverage points in the device. In this paper we propose a heuristic dynamic verification method based on a Binary Differential...
The employment of genetic algorithms in parameters optimization of direct-detection optical orthogonal frequency division multiplexing (DDO-OFDM) systems in short-range links is reported. Experimental transmission of a 3.56 Gb/s (4-QAM subcarrier mapping) optimized DDO-OFDM system in optical back-to-back (B2B) configuration and through 20 and 40 km of uncompensated standard single-mode fiber (SSMF)...
Differential evolution (DE) was originally designed to solve continuous optimization problems, but recent works have been investigating this algorithm for tackling combinatorial optimization (CO), particularly in permutation-based combinatorial problems. However, most DE approaches for combinatorial optimization are not general approaches to CO, being exclusive for per mutational problems and often...
This work presents four agents with different strategies to play a version of the 2-sided dominoes game, usually played in Minas Gerais state, Brazil. This incomplete information game must be played with two players and the goal is to discard all tiles first according to the rules. Each pair of agents was tested in a computational experiment, for 1,000,000 matches, in order to evaluate the individual...
This paper suggests an approach to develop a class of evolving neural fuzzy networks with adaptive feature selection. The approach uses the neo-fuzzy neuron structure in conjunction with an incremental learning scheme that, simultaneously, selects the input variables, evolves the network structure, and updates the neural network weights. The mechanism of the adaptive feature selection uses statistical...
It has been shown recently that unconstrained particles that follow the position and velocity update rules of a standard global best particle swarm optimization algorithm leave the boundaries of the search space within the first few iterations of the search process. Provided that a better solution does not exist outside of the search boundaries, these roaming particles are eventually pulled back within...
This paper presents a method of tracking sea surface targets in video using the WiSARD weightless neural network. The tracking of objects in video is an important and challenging task in many applications. Difficulties can arise due to weather conditions, target trajectory and appearance, occlusions, lighting conditions and noise. Tracking is a high-level application and requires the object location...
A number of empirical studies have compared the two extreme neighborhood topologies used in particle swarm optimization (PSO) algorithms, namely the star and the ring topologies. Based on these empirical studies, and also based on intuitive understanding of these neighborhood topologies, there is a faction within the PSO research community that advocates the use of the local best (lbest) PSO due to...
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...
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