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Prediction interval (PI) has been appeared as a promising tool to quantify the uncertainties and disturbances associated with point forecasts. Despite of its numerous applications in prediction problems, the use of PIs in control application is still limited. In this paper, a PI-based ANFIS controller is proposed and designed for nonlinear systems. In the proposed algorithm, a PI-based neural network...
Time series data are ubiquitous and are of importance in many application problems in engineering, science, medicine, economics and entertainment. Many real world pattern classification problems involve the processing and analysis of multiple variables in the temporal domain. These types of problems are referred to as Multivariate Time Series (MTS) problems. In many real-world applications, an MTS...
A novel fMRI classification method designed for rapid event related fMRI experiments is described and applied to the classification of loud reading of isolated words in Hebrew. Three comparisons of different grammatical complexity were performed: (i) words versus asterisks (ii) “with diacritics versus without diacritics” and (iii) “with root versus no root”. We discuss the most difficult task and,...
The primary aim of this work is to provide new tools for machine learning and reasoning within a framework of computing with holistic data representations. Specifically, we demonstrate recursive construction of mappings and functions in high-dimensional computing with random vectors — a connectionist computing model which provides benefits similar to neural networks, but allows compositional learning...
Minutiae, as the essential features of fingerprints, play a significant role in fingerprint recognition systems. Most existing minutiae extraction methods are based on a series of hand-defined preprocesses such as binarization, thinning and enhancement. However, these preprocesses require strong prior knowledge and are always lossy operations. And that will lead to dropped or false extractions of...
In this paper, we demonstrate nonlinear features extracted by deep neural network have better results in the task of dictionary learning. A nonlinear dictionary learning model is constructed and the optimization algorithm is developed. In the learning algorithm, we use the deep neural network to convey raw samples to feature space and learn a nonlinear dictionary. The extensive experimental results...
Deep learning scheme has received significant attention during these years, particularly as a way of building hierarchical representations from unlabeled data for a variety of signal and information processing tasks. However, deep neural networks suffer from slow learning speed since most used training algorithms are based on variations of the gradient descent algorithms which require iterative optimization...
Feature selection, instance selection and semi-supervised clustering are different challenges for machine learning and data mining communities. While other works have addressed each of these problems separately, in this paper we show how they can be addressed together, simultaneously. We propose an unified framework for semi-supervised co-selection of features and instances, based on weighting constrained...
With the development of deep learning, word vectors (i.e., word embeddings) have been extensively explored and applied to many Natural Language Processing tasks (e.g., parsing, Named Entity Recognition, etc). However, the semantic word vectors learned from context have insufficient sentiment information for performing sentiment analysis at different text levels. In this work, we present three Convolutional...
The problem of bridging the gap between image and natural language has gained more and more attention in recent years. This paper continues to push the study and improves the bidirectional retrieval performance across the modalities. Unlike previous works that target at single sentence densely describing the image objects, we extend the focus to associating deep image representations with noisy texts...
In this paper, we conduct a preliminary study about a newly established course evaluation survey given to the students by our institution. This survey contains several free-text questions which generates more qualitative but also voluminous unstructured feedback compared to the previous Likert scale question-based survey. Our aim is to apply data mining techniques to extract knowledge from these surveys,...
This paper studies Aspect-based Opinion Summarization (AOS) of reviews on particular products. In practice, an AOS system needs to address two core subtasks, aspect extraction and sentiment classification. Most existing approaches to aspect extraction, using linguistic analysis or topic modeling, are general across different products but not precise enough or suitable for particular products. Instead...
As a machine learning algorithms, deep learning algorithms developed in recent years, have been successfully practiced in many fields of computer vision, like face recognition, object detection and image classification. These Deep algorithms look for drawing out a very performing representation of the data, among which image and speech, through multi-layers in a deep hierarchical structure. In this...
The status of the insulators in power line can directly affect the reliability of the power transmission systems. Computer vision aided approaches have been widely applied in electric power systems. Inspecting the status of insulators from aerial images has been challenging due to the complex background and rapid view changing under different illumination conditions. In this paper, we propose a novel...
Multiple-kernel k-means (MKKM) clustering has demonstrated good clustering performance by combining pre-specified kernels. In this paper, we argue that deep relationships within data and the complementary information among them can improve the performance of MKKM. To illustrate this idea, we propose a diversity-induced MKKM algorithm with extreme learning machine (ELM)-based feature extracting method...
This paper presents a pruned sparse extreme learning machine (PS-ELM) algorithm, which can generate a compact single-hidden-layer neural network (SLNN) by automatically pruning the number of hidden nodes while keep high accuracy. In this PS-ELM algorithm, input connections between input and hidden layers are base vectors, which can sparsely map the input features into hidden layer by using gradient...
Automatic Offline Handwritten Signature Verification has been researched over the last few decades from several perspectives, using insights from graphology, computer vision, signal processing, among others. In spite of the advancements on the field, building classifiers that can separate between genuine signatures and skilled forgeries (forgeries made targeting a particular signature) is still hard...
In this paper, we propose a deep convolutional neural network solution to the analysis of image data for the detection of rail surface defects. The images are obtained from many hours of automated video recordings. This huge amount of data makes it impossible to manually inspect the images and detect rail surface defects. Therefore, automated detection of rail defects can help to save time and costs,...
Deep Belief Network (DBN) learns the features of the raw data automatically, and develops a new idea for the study of fault analysis of High Speed Train (HST). Combining deep learning and classification ensemble technology, this paper presents a novel DBN hierarchical ensemble model for HST fault analysis. Firstly, Fast Fourier Transform (FFT) coefficients of the HST vibration signals are extracted...
The performance of most conventional classification systems relies on appropriate data representation and much of the efforts are dedicated to feature engineering, a difficult and time-consuming process that uses prior expert domain knowledge of the data to create useful features. On the other hand, deep learning can extract and organize the discriminative information from the data, not requiring...
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