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A Particle Swarm Optimization (PSO) technique, in conjunction with Fuzzy Adaptive Resonance Theory (ART), was implemented to adapt vigilance values to appropriately compensate for a disparity in data sparsity. Gaining the ability to optimize a vigilance threshold over each cluster as it is created is useful because not all conceivable clusters have the same sparsity from the cluster centroid. Instead...
In this paper we introduce the Tensor Deep Stacking Network (T-DSN) Toolkit, an implementation of the T-DSN deep learning architecture. The toolkit consists of a Python library and a set of accompanying helper scripts that allow you to train and evaluate T-DSN models. The toolkit is designed to be portable, modular, efficient and parallelized. Our goal for the toolkit is to promote research on this...
Multi-layer feed-forward neural networks are commonly used in supervised learning, for which data training is required. One popular way to check whether the training is completed is to monitor the mean square error. It is expected that the learning is completed when the mean square error is less than or equal to an error threshold, which is usually a very small positive real number (e.g., 0.001)....
Support Vector Data Description (SVDD) is a well-known supervised learning method for novelty detection purpose. For its classification task, SVDD requires a fully-labeled dataset. Nonetheless, contemporary datasets always consist of a collection of labeled data samples jointly a much larger collection of unlabeled ones. This fact impedes the usage of SVDD in the real-world problems. In this paper,...
In this paper, we consider a linear supervised dimension reduction method for classification settings: Stochastic Discriminant Analysis. This method matches point similarities in the projection space with those in a response space. These similarities are represented by t-distributed joint pairwise probabilities. The matching is done by minimizing the Kullback-Leibler divergence between the two probability...
Dialogue act recognition is recognized as an important step for computers to understand human dialogues as it is closely related to the human intention. There are two main challenges in dialogue act recognition. Firstly, multimodal features should be taken into consideration, which include lexical, syntactic, prosodic cues, even facial appearance and gesture. Secondly, samples distribution in the...
The k-nearest neighbor method generates predictions for a particular instance from its neighborhood. It is a simple but effective supervised method for classification. However, the traditional k-nearest neighbor algorithm using the majority voting rule for the class label usually loses a part of useful information in the neighborhood. This paper tries to learn from the neighborhood for more useful...
Extreme Learning Machine (ELM) is an elegant technique for training Single-hidden Layer Feedforward Networks (SLFNs) with extremely fast speed that attracts significant interest recently. One potential weakness of ELM is the random generation of the input weights and hidden biases, which may deteriorate the classification accuracy. In this paper, we propose a new Memetic Algorithm (MA) based Extreme...
In the application of cooperative coevolution for neuro-evolution, problem decomposition methods rely on architectural properties of the neural network to divide it into subcomponents. During every stage of the evolutionary process, different problem decomposition methods yield unique characteristics that may be useful in an environment that enables solution sharing. In this paper, we implement a...
1-norm support vector machine (SVM) has attracted substantial attentions for its good sparsity. However, the computational complexity of training 1-norm SVM is about the cube of the sample number, which is high. This paper replaces the hinge loss or the ε-insensitive loss by the squared loss in the 1-norm SVM, and applies orthogonal matching pursuit (OMP) to approximate the solution of the 1-norm...
Researches with ensemble Systems have emerged as an attempt to obtain a computational system that works with classification tasks in an efficient way. The main goal of using ensemble systems is to improve the performance of a pattern recognition system in terms of better generalization and/or of clearer design. One of the main challenges in the design of a ensemble system is the definition of the...
Over the last years, researchers have focused their attention on a new approach, supervised clustering, that combines the main characteristics of both traditional clustering and supervised classification tasks. Motivated by the importance of the initialization in the traditional clustering context, this paper explores to what extent supervised initialization step could help traditional clustering...
The major issue of researchers in ANN field is the optimization of the training process including time cost and NN structure. In response to the long training time, Multi-Agent architecture of feed forward Flexible Neural Tree model (MAFNT) is introduced for parallelizing the NN training. Moreover, looking for the best topology of NN, for a given problem, accounts for the large feasible solutions...
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