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There exists a base classification system for classification of problem tickets in the Enterprise domain. Different deep learning algorithms (Gated Recursive Unit and Long Short Term Memory) were investigated for solving the classification problem. Experiments were conducted for different parameters and layers for these algorithms. Paper brings out the architectures tried, results obtained, our conclusions...
This paper presents regional Support Vector Machine (SVM) classifiers with a spatial model for object detection. The conventional SVM maps all the features of training examples into a feature space, treats these features individually, and ignores the spatial relationship of the features. The regional SVMs with a spatial model we propose in this paper take into account a 3-dimentional relationship...
Research that explores the use of machine learning for automatic security classification of information objects is about to emerge. In this paper we investigate the opportunity to increase the machine learning performance by taking advantage from time information that is "hidden" in the documents of the training set. This paper presents a technique to do so, and confirms that this is a promising...
In this paper, we develop the max-margin similarity preserving factor analysis (MMSPFA) model. MMSPFA utilizes the latent variable support vector machine (LVSVM) as the classification criterion in the latent space to learn a discriminative subspace with max-margin constraint. It jointly learns factor analysis (FA) model, similarity preserving (SP) term and max-margin classifier in a united Bayesian...
Lending loans to borrowers is considered one of the main profit sources for banks and financial institutions. Thus, careful assessment and evaluation should be taken when deciding to grant credit to potential borrowers. With the rapid growth of credit industry and the massive volume of financial data, developing effective credit scoring models is very crucial. The literature in this area is very dense...
Tens of thousands of pictures are taken at different locations throughout the year. People often visit places and take pictures to remember their visits. We believe that the seasonal travel patterns of people to specific locations will create a correlation between a location and the season of the images taken in that location. For example, fewer people visit Bear Valley, California during the summer...
The field of network and computer security is a never-ending race with attackers, trying to identify and patch software vulnerabilities before they can be exploited. In this ongoing conflict, it would be quite useful to be able to predict when and where the next software vulnerability would appear. The research presented in this paper is the first step towards a capability for forecasting vulnerability...
Routability is one of the primary objectives in placement. There have been many researches on forecasting routing problems and improving routability in placement but no perfect solution is found. Most traditional routability-driven placers aim to improve global routing result, but true routability lies in detailed routing. Predicting detailed routing routability in placement is extremely difficult...
This paper introduces an enhancement to linguistic forecast representation using Triangular Fuzzy Numbers (TFNs) called Enhanced Linguistic Generation and Representation Approach (ElinGRA). Since there is always an error margin in the predictions, there is a need to define error bounds in the forecast. The interval of the proposed presentation is generated from a Fuzzy logic based Lower and Upper...
The worldwide increase in the integration of photovoltaic generation has necessitated improvements in the forecasting approaches. Two models are proposed to cater for PV generation forecasts for few minutes to several hours look-ahead times. A very fast and accurate prediction model based on extreme learning machine is deployed for day-ahead prediction. Moreover, an adaptive and sequential model is...
A promising solution to reduce the testing costs of analog/RF circuits is the alternate test strategy, which permits to replace costly specification measurements by simple low-cost indirect measurements. This approach has been widely explored and demonstrated in the literature on various case studies over the past twenty years. However it is clear that the efficiency of this strategy strongly depends...
In multiclass classification problems we face the challenge of having many binary classifiers. Consulting this large number of classifiers might be confusing and time consuming. In this paper, we propose a new framework for training and prediction in multiclass problems. In this framework, we perform traditional training. Next we map training examples to prediction models. Finally we produce the Example...
Web service recommendation plays an important role in building reliable service-oriented systems for both the service providers and the active users. However, with the proliferation of web services on the World Wide Web, traditional service recommendation is hard to accurately provide customized services to active users. In this paper, we propose a novel web service recommender model using collaborative...
Systems need to know the physical locations of objects and people to optimize user experience and solve logistical and security issues. Also, there is a growing demand for applications that need to locate individual assets for industrial automation. This work proposes an indoor positioning system (IPS) able to estimate the item-level location of stationary objects using off-the-shelf equipment. By...
Optimizing the weighting of features significantly improves the predictions in regression tasks. In this paper, we employ evolution strategies to evolve distance measures in a spatio-temporal regression approach for short-term wind prediction. The well-understood nearest neighbor regression method is the basis of our study. We compare a classic feature selection approach based on binary representations...
Monitoring the boiling point of a diesel fuel is an important step to understand the characteristics of the diesel fuel. This study evaluated the feasibility of adaptive linear neuron (Adaline) as a predictive model to predict the boiling point of diesel fuel based on near infrared spectrum. The parameters of learning rate and training cycle that involved in the optimization process were examined...
Classification is a central problem in the fields of data mining and machine learning. Using a training set of labelled instances, the task is to build a model (classifier) that can be used to predict the class of new unlabelled instances. Data preparation is crucial to the data mining process, and its focus is to improve the fitness of the training data for the learning algorithms to produce more...
Forecasting daily returns volatility is crucial in finance. Traditionally, volatility is modelled using a time-series of lagged information only, an approach which is in essence atheoretical. Although the relationship of market conditions and volatility has been studied for decades, we still lack a clear theoretical framework to allow us to forecast volatility, despite having many plausible explanatory...
We present an accurate, real-time approach to robotic grasp detection based on convolutional neural networks. Our network performs single-stage regression to graspable bounding boxes without using standard sliding window or region proposal techniques. The model outperforms state-of-the-art approaches by 14 percentage points and runs at 13 frames per second on a GPU. Our network can simultaneously...
This paper proposes a pre-processing method to enhance the accuracy of wind power forecast. Instead of using the whole dataset indifferently for training, the proposed method only uses the segments that share the same pattern. In order to search for such segments in the historical data, a k-OCCO filter and a weighted multi-resolution morphological gradient (MMG) are employed. Afterwards, the forecast...
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