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Soft sensor are widely used to estimate process variables which are difficult to measure online in industrial process control. This paper proposes a new soft sensor modeling method based on a deep learning method, which integrates denoising auto-encoders (DAE) with support vector regression (SVR) method. The denoising auto-encoders are designed to capture robust high-level feature representation of...
Machine learning classifiers are widely used for text categorization however a classifier misclassifies some of the instances into a category that is relevant to their actual category. The categorization ability of a classifier can be improved by filtering dataset with better classifier and removing such category for misclassified instances. In this paper we proposed a two level approach where level-1...
Many classification algorithms, such as Neural Networks and Support Vector Machines, have a range of hyper-parameters that may strongly affect the predictive performance of the models induced by them. Hence, it is recommended to define the values of these hyper-parameters using optimization techniques. While these techniques usually converge to a good set of values, they typically have a high computational...
Classification is one of the most common machine learning tasks. SVMs have been frequently applied to this task. In general, the values chosen for the hyper-parameters of SVMs affect the performance of their induced predictive models. Several studies use optimization techniques to find a set of hyper-parameter values that induces classifiers with good predictive performance. This paper investigates...
Leader identification is a crucial task in social analysis, crowd management and emergency planning. In this paper, we investigate a computational model for the individuation of leaders in crowded scenes. We deal with the lack of a formal definition of leadership by learning, in a supervised fashion, a metric space based exclusively on people spatiotemporal information. Based on Tarde's work on crowd...
Face recognition has been widely used in many application areas such as photo album management and information security. Rapid growth of handheld devices and social networks bring new challenges to face recognition algorithm design and system engineering. To be effective on a handheld device, the face recognition model must be simple and lightweight, and also needs to handle the large variations in...
In this article a new scheme is proposed to use mean supervector in text-prompted speaker verification system. In this scheme, for each month name a subsystem is constructed and a final score based on passphrase is computed by the combination of the scores of these subsystems. Results from the telephony dataset of Persian month names show that the proposed method significantly reduces EER in comparison...
Support Vector Regression (SVR) is a flexible regression method, which can be applied directly to NARMAX system identification models. SVR is a one-step convex optimisation process which attempts to maximise generalisation performance. This paper compares SVR performance with that of multi-layer perceptrons and radial basis function networks for varying numbers of time lags included in the model.
Recently there has been great interest in the application of word representation techniques to various natural language processing (NLP) scenarios. Word representation features from techniques such as Brown clustering or spectral clustering are generally computed from large corpora of unlabeled data in a completely unsupervised manner. These features can then be directly included as supplementary...
Constructing accurate models that represent the underlying structure of Big Data is a costly process that usually constitutes a compromise between computation time and model accuracy. Methods addressing these issues often employ parallelisation to handle processing. Many of these methods target the Support Vector Machine (SVM) and provide a significant speed up over batch approaches. However, the...
Using the framework of Reproducing Kernel Hilbert Spaces, we develop a new sequence kernel that measures similarity between sequences of observations. We then apply it to a text-independent speaker verification task using the NIST 2004 Speaker Recognition Evaluation database. The results show that incorporating our new sequence kernel in an SVM training architecture not only yields performance significantly...
In this paper, we propose using local learning for multiclass novelty detection, a framework that we call local novelty detection. Estimating the novelty of a new sample is an extremely challenging task due to the large variability of known object categories. The features used to judge on the novelty are often very specific for the object in the image and therefore we argue that individual novelty...
In this paper, we develop a novel framework for action recognition in videos. The framework is based on automatically learning the discriminative trajectory groups that are relevant to an action. Different from previous approaches, our method does not require complex computation for graph matching or complex latent models to localize the parts. We model a video as a structured bag of trajectory groups...
We address the training problem of the sparse Least Squares Support Vector Machines (SVM) using compressed sensing. The proposed algorithm regards the support vectors as a dictionary and selects the important ones that minimize the residual output error iteratively. A measurement matrix is also introduced to reduce the computational cost. The main advantage is that the proposed algorithm performs...
Restricted Boltzmann Machines (RBMs) have been developed for a lot of applications in the past few years, and many of its variants have also appeared. In this paper, RBM model and its learning algorithm with contrastive divergence algorithm will be introduced firstly. Then three important variants of RBM are presented in details, which are sparse RBM, discriminative RBM, and the Deep Boltzmann Machines...
Recently, many information retrieval (IR) based bug localization approaches have been proposed in the literature. These approaches use information retrieval techniques to process a textual bug report and a collection of source code files to find buggy files. They output a ranked list of files sorted by their likelihood to contain the bug. Recent approaches can achieve reasonable accuracy, however,...
Human action recognition from video input has seen much interest over the last decade. In recent years, the trend is clearly towards action recognition in real-world, unconstrained conditions (i.e. not acted) with an ever growing number of action classes. Much of the work so far has used single frames or sequences of frames where each frame was treated individually. This paper investigates the contribution...
Parallel computing is a simultaneous use of multiple compute resources such as processors to solve difficult computational problems. It has been used in high-end computing areas such as pattern recognition, defense, web search engine, and medical diagnosis. This paper focuses on the implementation of pattern classification technique, Support Vector Machine (SVM) using Symmetric Multi-Processor (SMP)...
Side-channel attack (SCA) is a very efficient cryptanalysis technology to attack cryptographic devices. It takes advantage of physical information leakages to recover the cryptographic key. In order to strengthen the power to extract the cryptographic key-relevant information, this article introduces the Support Vector Machine technologies. Taking a software implementation of masked AES-256 on an...
Todays, feature selection is an active research in machine learning. The main idea of feature selection is to select a subset of available features, by eliminating features with little or no predictive information. This paper presents a hybrid model with a new local search technique based on reinforcement learning for feature selection. We combined the particle swarm optimization (PSO) with support...
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