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This paper presents a Transductive Support Vector Machine (TSVM) with quasi-linear kernel based on a clustering assumption for semi-supervised classification. Since the potential separating boundary is located in low density area between classes, a modified density clustering method by considering label information is firstly introduced to extract the information of potential separating boundary in...
Multi-label performance evaluation metrics could be mainly grouped into two parts: ranking-based and instance-based. The former is based on discriminant function values (e.g., average precision and ranking loss). The latter is associated with predicted relevant label subsets (e.g., Hamming loss and accuracy), which is determined via a proper threshold from the discriminant function values. Firstly,...
In order to precisely study the advertisers' bidding behavior, in this paper, we proposed a HMM(Hidden Markov Model) forecasting model using the historical auction data of advertisers, and predicted the advertisers' bidding sequences in the future with this model. In the process of establishing HMM model for advertisers' bidding behavior, we define the bidding as the hidden variable, and define the...
Magnetic induction tomography (MIT) is a non-invasive technology for visualization of the conductivity distribution inside inhomogeneous media. So far, the resolution of MIT has not been high enough for practical applications in biomedical imaging yet. In this research, we investigate the image reconstruction problem using statistical classification method to enhance the resolution of MIT. First,...
Extreme Learning Machine (ELM) has been introduced as a new algorithm for training single hidden layer feed-forward neural networks (SLFNs) instead of the classical gradient-based algorithms. Based on the consistency property of data, which enforce similar samples to share similar properties, ELM is a biologically inspired learning algorithm with SLFNs that learns much faster with good generalization...
The automated understanding of people activities from video sequences is an open research topic in which the computer vision and pattern recognition areas have made big efforts in recent years. This paper proposes the Self Organizing Activity Description Map (SOADM). It is a novel neural network based on the self-organizing paradigm to classify high level of semantic understanding from video sequences...
The identification of predictive biomarkers of complex disease with robustness and specificity is an ongoing challenge. Gene expressions provide information on how the cell reacts to a particular state and the relationship of genes may lead to novel information. A network-based approach integrating expression data with protein-protein interaction network can be used to identify gene-subnetwork biomarkers...
This paper presents a novel dimensionality reduction method, called uncorrelated transferable feature extraction (UTFE), for signal classification in brain-computer interfaces (BCIs). Considering the difference between the source and target distributions of signals from different subjects, we construct an optimization objective that finds a projection matrix to transform the original data in a high-dimensional...
Accurate forecasting of wind power generation is quite an important as well as challenging task for the system operators and market participants due to its high uncertainty. It is essential to quantify uncertainties associated with wind power generation forecasts for their efficient application in optimal management of wind farms and integration into power systems. Prediction intervals (PIs) are well...
Encoded Neural Networks (ENN) associate low-complexity algorithm with a storage capacity much larger than Hopfield Neural Networks' (HNN) for the same number of nodes. They are thus promising for implementing large scale neural networks mimicking the functioning of the human brain. The implementation of such a network on chip requires reducing the power consumption of the nodes to the femtojoule range...
Frequency estimation in three-phase power systems is considered from a state space point of view, and a robust and fast converging algorithm for estimating the fundamental frequency of three-phase power systems is introduced. This is achieved by exploiting the Clarke transform to incorporate the information from all the phases and then designing a widely linear state space estimator that can accurately...
This paper presents the design and simulation results of a silicon cochlea system that has closely similar behavior as the real cochlea. A cochlea filter-bank based on the improved three-stage filter cascade structure is used to model the frequency decomposition function of the basilar membrane; a filter tuning block is designed to model the adaptive response of the cochlea; besides, an asynchronous...
In this paper, we propose a new online system to detect malicious spam emails and to adapt to the changes of malicious URLs in the body of spam emails by updating the system daily. For this purpose, we develop an autonomous system that learns from double-bounce emails collected at a mail server. To adapt to new malicious campaigns, only new types of spam emails are learned by introducing an active...
In this study, a lightweight kernel regression algorithm for embedded systems is proposed. In our previous study, we proposed an online learning method with a limited number of kernels based on a kernel regression model known as a limited general regression neural network (LGRNN). The LGRNN behavior is similar to that of k-nearest neighbors except for its continual interpolation between learned samples...
In this paper, a neural-network (NN) observer-based optimal control solution for unknown nonlinear systems with control constraints using adaptive dynamic programming (ADP) is considered. First, to confront the unknown system, a NN observer is designed to estimate system states. Second, to deal with the control constraints, a quasi-norm performance index function is introduced. Third, based on the...
Echo State Networks (ESNs) have been applied to time-series data arising from a structural health monitoring multi-sensor array placed onto a test footbridge which has been subjected to a number of potentially damaging interventions over a three year period. The time-series data, sampled approximately every five minutes from ten temperature sensors, have been used as inputs and the ESNs were tasked...
In this paper, the use of multi label neural networks are proposed for detection of temporally overlapping sound events in realistic environments. Real-life sound recordings typically have many overlapping sound events, making it hard to recognize each event with the standard sound event detection methods. Frame-wise spectral-domain features are used as inputs to train a deep neural network for multi...
In this paper, we explore the capabilities of a sound classification system that combines both a novel FPGA cochlear model implementation and a bio-inspired technique based on a trained convolutional spiking network. The neuromorphic auditory system that is used in this work produces a form of representation that is analogous to the spike outputs of the biological cochlea. The auditory system has...
In some practical classification problems in which the number of instances of a particular class is much lower/higher than the instances of the other classes, one commonly adopted strategy is to train the classifier over a small, balanced portion of the training data set. Although straightforward, this procedure may discard instances that could be important for the better discrimination of the classes,...
Throughout the history, insects had been intimately connected to humanity, in both positive and negative ways. Insects play an important part in crop pollination, on the other hand, some of them spread diseases that kill millions of people every year. Effective control of harmful insects while having little impact to beneficial insects and environment is extremely important. Recently, an intelligent...
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