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Diversity is deemed to be a key issue in classifier combination. For this reason, not every classifier is an expert for every query pattern. Thus, many researchers have focused on dynamic ensemble selection. Most works, however, use only one criterion to perform the dynamic selection. Hence, multiple criteria can provide a decision more effective than the one produced by any of the criteria. Another...
sEMG (surface electromyography) signals have been used as human-machine interface to control robots or prostheses in recent years. sEMG-based torque estimation is a widely research methodology to obtain human motion intention. Most researches focus on improving the accuracy of sEMG-torque models, which often makes them complicated and confined in the laboratory research. However, an accurate estimation...
This paper proposes the implementation of a Support Vector Machines (SVM) for automatic recognition of numerical speech commands. Besides the pre-processing of the speech signal with Mel Frequency Ceptral Coefficients (MFCC), is used to Discrete Cosine Transform (DCT) to generate a two-dimensional matrix used as input to SVM algorithm for generating the pattern of words to be recognized. The Support...
Neural correlates corresponding to a specific cognitive tasks has been made possible with techniques like functional magnetic resonance imaging. The increasing number of neuroimaging studies has made meta-analysis methods popular for useful inferencing across multiple studies. The easy availability of neuroinformatic tools has also resulted in increasing the number of meta-analysis studies. We compare...
In this paper we consider a network of phase oscillators. We develop the equations that model the time evolution of the phase of each oscillator in the network. The oscillator represents a modified Kuramoto oscillator and in this study we discuss how these modifications are obtained. In the context of this study, we use this network to model a network of PLLs for distributed clock applications. We...
This paper presents Quaternionic Bidirectional Auto-Associative Memory (QBAAM) that is an associative memory network storing patterns with multiple levels. A part of neurons in the network are quaternionic neurons, where their states are encoded by quaternion, which is a four-dimensional hypercomplex number system. These neurons can represent three kinds of discretized phases, i.e., three-dimensional...
Control and monitoring of indoor thermal conditions represent crucial tasks for people's satisfaction in working and living spaces. Among all standards released, predicted mean vote (PMV) is the international index adopted to define users thermal comfort conditions in thermal moderate environments. PMV is a nonlinear function of various quantities, which generally limits its applicability to the heating,...
Linear discriminant analysis seeks to find a one-dimensional projection of a dataset to alleviate the problems associated with classifying high-dimensional data. The earliest methods, based on second-order statistics often fail on multimodal datasets. Information-theoretic criteria do not suffer in such cases, and allow for projections to spaces higher than one dimension and with multiple classes...
In this paper, design of a neural network for a domain-specific problem is described. The problem of concern is forecasting flood events where data is contaminated heavily by noise, training examples have different importance levels and noisy data coincides with the most important ones. To this end, two ideas are explored namely, changing the loss function and integrating a coefficient that reflects...
Electromyography (EMG) signals can be used to integrate with machines and form one assistive system such as a powered exoskeleton. This paper focuses on the design and development of a low-cost elbow joint powered exoskeleton for human power augmentation, controlled by the EMG. Majority of the hardware has been designed and developed in-house, without using expensively available hardware. A theoretical...
This paper presents a memory crossbar based on two serial memristors with threshold characteristic to eliminate the effect of sneak paths, which is a key issue in crossbar memory system leading to great degradation in their performance and power efficiency. At first, we analyze the threshold characteristic of memristor and propose a memristor model with threshold. Based on this model, the paper presents...
In this paper, we develop a novel approximate policy iteration reinforcement learning algorithm with unsupervised feature learning based on manifold regularization. The proposed algorithm can automatically learn data-driven smooth basis representations for value function approximation, which can preserve the intrinsic geometry of the state space of Markov decision processes. Moreover, it can provide...
This paper proposes a supervised approach for analysis of high-dimensional data using low-dimensional submanifolds. This method offers many useful properties. Using first order approximation for the given nonlinear mapping, we introduce a locally linear model. This model is such that it minimizes the local approximation error resulted by mapping to a local subspace during the learning. Additionally,...
In many real-world applications such as image classification, labeled training examples are difficult to obtain while unlabeled examples are readily available. In this context, semi-supervised learning methods take advantage of both labeled and unlabeled examples. In this paper, a greedy graph-based semi-supervised learning (GGSL) approach is proposed for multi-class classification problems. The labels...
Feature selection plays an important role in pattern classification. It is especially an important preprocessing task when there are large number of features in comparison to number of patterns as is the case with gene expression data. A new unsupervised feature selection method has been evolved using autoencoders since autoencoders have the capacity to learn the input features without class information...
This paper proposes a method to estimate temporally accurate human pulse peaks for noncontact pulse transit time (PTT) measurements. The PTT is considered as a significant diagnostic index for conditions such as blood pressure and arterial stiffness; however, millisecond-order accuracy is required in the determination of each pulse peak. In this study, human pulse waveforms are obtained from wrist...
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)....
This paper proposes an improved SVM based multi-label classification method by using relationship among labels. Following a traditional multi-label solution, binary relevance (BR) method is first used to decompose the multi-label classification problem into multiple binary classification sub-problems, each of which is solved by an SVM classifier. By using Platt's sigmoid technique, each SVM classifier...
We use computational simulations to analyse the behavior of the recently proposed Bidirectional Activation-based Learning algorithm (BAL) which was inspired by the Generalized Recirculation algorithm (GeneRec). Both algorithms avoid biologically implausible backpropagation of the error signal, and instead use propagation of neuron activations, which drive the weight updates, using only local variables...
This paper introduces Lie algebra-valued feedforward neural networks, for which the inputs, outputs, weights and biases are all from a Lie algebra. This type of networks represents an alternative generalization of the real-valued neural networks besides the complex-, hyperbolic-, quaternion-, and Clifford-valued neural networks that have been intensively studied over the last few years. The full deduction...
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