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In this paper, we propose a new semi-supervised growing neural gas (GNG) model, named Consensus-Based Semi-Supervised GNG, or CSSGNG, in which both labeled and unlabeled data are used to train the network. In contrast to former adaptations of the GNG to semi-supervised classification, such as the SSGNG and OSSGNG models, the CSSGNG does not assign a single scalar label value to each neuron. Instead...
This paper describes memristor-based neuromorphic circuits for non-linear separable pattern recognition. We initially describe a memristor based neuron circuit and then show how multilayer neural networks can be constructed using this neuron circuit. These neuromorphic circuits are capable of learning both linearly and non-linearly separable logic functions. This paper presents the first study of...
In order to improve corrosion resistance of alloy S355 EN 1025, the relationship between the thickness of zinc coating created during the process of acidic galvanic zincing and factors that influence this process were investigated. Influence of individual factors on thickness of zinc coating for sample area with surface current density of 3 A·dm−2 was determined by planned experiment which uses central...
This paper first builds a rule-based fuzzy representation of shape context and then present a multi-clue based fuzzy shape context approach (MFSC) using combination of geometric information and graph transduction. The MFSC takes complexity of object shape into account. In this approach, the distance between arbitrary two sampled points on any shape is redefined and graph transduction is used to correct...
The state-of-the-art classification algorithms rarely consider the relationship between the attributes in the data sets and assume the attributes are independently to each other (IID). However, in real-world data, these attributes are more or less interacted via explicit or implicit relationships. Although the classifiers for class-balanced data are relatively well developed, the classification of...
By exchanging propositional information, agents can implicitly reduce large domain state spaces, a feature that is particularly attractive for Reinforcement Learning approaches. This paper proposes a learning technique that combines a Reinforcement Learning algorithm and a planner for propositionally constrained state spaces, that autonomously help agents to implicitly reduce the state space towards...
In this paper a method for nonparametric regression estimation in non-stationary environment is presented. The Parzen kernels are used to design the recursive general regression neural networks to track changes of non-stationary system under non-stationary noise. The probabilistic properties of the proposed method are investigated. Experimental results are presented and discussed.
Automatic classification of structured data is a challenging task and its relevance to many domains is evident. However, collecting labeled data may turn to be a quite expensive task and sometimes even prone to mislabeling. A technical solution to this problem consists in combining few labeled data samples and a significant amount of unlabeled data samples to train a classifier. Likewise, the present...
A practical roadmap to human-level artificial general intelligence is outlined, leading from the current situation in which complex AGI-oriented cognitive architectures remain partially implemented and inadequately tested, to a future in which AGI systems are deployed to carry out a variety of practical tasks currently only achievable via humans. The roadmap involves simultaneous development of proto-AGI...
Decision trees are the commonly applied tools in the task of data stream classification. The most critical point in decision tree construction algorithm is the choice of the splitting attribute. In majority of algorithms existing in literature the splitting criterion is based on statistical bounds derived for split measure functions. In this paper we propose a totally new kind of splitting criterion...
A stochastic blockmodel is a generative model for blocks, where a block is a set of coherent nodes and relations between the nodes are explained by the corresponding pair of blocks. Most existing methods make use of both the presence and the absence of links between nodes, encoded by the adjacency matrix, to learn the corresponding models. In this paper, we present a new method in which we use only...
Uncertainty is known to be a concomitant factor of almost all the real world commodities such as oil prices, stock prices, sales and demand of products. As a consequence, forecasting problems are becoming more and more challenging and ridden with uncertainty. Such uncertainties are generally quantified by statistical tools such as prediction intervals (Pis). Pis quantify the uncertainty related to...
Spike sorting plays an important role in analysing electrophysiological data and understanding neural functions. Developing spike sorting methods that are highly accurate and computationally inexpensive is always a challenge in the biomedical engineering practice. This paper proposes an automatic unsupervised spike sorting method using the landmark-based spectral clustering (LSC) method in connection...
This paper discusses the stability condition for discrete time recurrent neural networks (RNNs) with complex-valued linear threshold (CLT) neurons. The energy-function method is very useful for complex-valued RNNs study, especially for multi-stable RNNs. In addition to properties of CLT RNNs discussed in earlier work, a new stability condition is offered here by virtue of a lower-bounded energy function...
Vehicle side slip angle is a critical variable used in car safety systems like Electronic Stability Control. Due to the practical difficulty in direct measurement of side slip angle, accurate estimation of vehicle side slip angle using available signals is becoming important. This paper presents a novel algorithm for estimating the side slip angle of a vehicle in real time using inertial motion sensors...
Micro-expression is a very short and rapid involuntary facial expression, which reveals suppressed affect. Recognizing micro-expression can help to accurately grasp the real feelings of people, a result that can have an important practical impact. But the scholars' studies have demonstrated that real micro-expression is difficult to identify. There are two main restrictive factors, one is the need...
The nonnegative convex polyhedral cone (NCPC) learning is discussed in this paper. By exploiting the multiplicative update nonnegative quadratic programming, a multiplicative update algorithm is developed for NCPC learning. The proposed algorithm is promising for nonnegative matrix factorization (NMF) and we verify this by numerical experiments.
This study aims at finding the relationship between EEG signals and human emotional states. Movie clips are used as stimuli to evoke positive, neutral and negative emotions of subjects. We introduce a new effective classifier named discriminative graph regularized extreme learning machine (GELM) for EEG-based emotion recognition. The average classification accuracy of GELM using differential entropy...
The mammalian cochlear consists of nonlinear components: lymph (viscous fluid), a basilar membrane (vibrating membrane), outer hair cells (active dumpers), inner hair cells (neural transducers), and spiral ganglion cells (parallel spikes density modulators). In this paper, a novel spiral ganglion cell model based on an asynchronous sequential logic is presented. It is shown that the presented model...
Recently, it has been shown that a probabilistic model based on two of the main concepts in quantum physics — a density matrix and the Born rule, can be suitable for the modeling of learning algorithms in biologically plausible artificial neural networks framework. It has been shown that the proposed probabilistic interpretation is suitable for modeling on-line learning algorithms for Independent...
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