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Handwritten digit recognition is a typical image classification problem. Convolutional neural networks, also known as ConvNets, are powerful classification models for such tasks. As different languages have different styles and shapes of their numeral digits, accuracy rates of the models vary from each other and from language to language. However, unsupervised pre-training in such situation has shown...
This paper proposes a hybrid deep learning algorithm, namely, the Deep Boltzmann Functional Link Network (DBFLN) for classification problems. A Deep Boltzmann Machine (DBM) with two layers of Restricted Boltzmann Machine is the generative model that is used to generate stochastic features and input weights for the discriminative model. A discriminative Functional Link Network (FLN) uses these features...
Dendrite morphological neurons are a type of artificial neural network that work with min and max operators instead of algebraic products. These morphological operators allow each dendrite to build a hyper-box in classification N-dimensional space. In contrast with classical perceptrons, these simple geometrical representations, hyper-boxes, allow the proposal of training methods based on heuristics...
A social spammer detection model based on tri-training (SSDTT) is adopted. The main procedure of the work is: First, train three original classifiers with a small amount of labeled data. Then, select confident users that are labeled for a classifier if the other two classifiers agree on the labeling as new training data. Afterwards, repeat these steps until three classifiers are not updated. Experimental...
Botnets represent one of the most destructive cybersecurity threats. Given the evolution of the structures and protocols botnets use, many machine learning approaches have been proposed for botnet analysis and detection. In the literature, intrusion and anomaly detection systems based on unsupervised learning techniques showed promising performances. In this paper, we investigate the capability of...
Automatic restaurant attribute classification is an instance of multi-label learning. Users upload hundreds of photos along with textual reviews on websites like Yelp. The users also have the option of labelling the businesses with specific attributes such as if it is good for kids or if it has table service. In our work, we explore a variety of methods to label businesses with attributes using just...
Modern data is increasingly complex. High dimensionality, heterogeneity and independent multiple representations are the basic properties of today's data. With increasing sources of data collection, a single object can have multiple representations, which we call views. In this paper we propose a multiview classification technique, which uses fuzzy mapping to obtain maximum similarity between an object...
The study of compound-target binding profiles has been a central theme in cheminformatics. For data repositories that only provide positive binding profiles, a popular assumption is all unreported profiles are negative. In this paper, we caution audience not to take such assumption for granted. Under a problem setting where binding profiles are used as features to train predictive models, we present...
Robust vessel segmentation of fundus images is of great interest for better diagnosis of many diseases like diabetic retinopathy, retinopathy of prematurity, vein occlusions and so on. In this paper, we propose a novel example-based vessel segmentation method, based on learning the mapping relationship between fundus images and their corresponding ground truths. Firstly, the training images and their...
Naive Bayes classifiers are widely used to filter spam emails, however, the strong independence assumptions between features limit their performance in accurately identifying spams. To address this issue, we proposed a support machine vector based naive Bayes — SVM-NB — filtering system. The SVM-NB first constructs an optimal separating hyperplane that divides samples in the training set into two...
Cooperative behavior enhances an organization's ability to reach goals. The present study investigated whether and how prepaid incentives and promised incentives influenced cooperation differently among organization members in an experiment. Results showed that promised incentives appear more effective in terms of cooperative behavior than prepaid incentives, and the perception of benefit is a mediator...
Enterprise Resource Planning (ERP) systems have established a reputation in the world of business as indispensables tools that integrate all departments and functions across a company into a single computer system. However, implementing an ERP system does not always result in enhanced organizational performance. In order to ensure successful implementation, companies should study the critical factors...
In the paper, the deep evolving neural network and its learning algorithms (in batch and on-line mode) are proposed. The deep evolving neural network's architecture is developed based on GMDH approach (in J. Schmidhuber's opinion it is historically first system, which realizes deep learning ) and least squares support vector machines with fixed number of the synaptic weights, which provide high quality...
In this communication we explain how a Support Vector Machine (SVM) can be applied to compute the Euler number or Genus of a 2-D binary image. By taking into account the results provided by a mathematical formulation that is known producing exact results we derive two specialized SVM-based architectures, one useful for the 4-connected case and one useful for the 8-connected case. We validate the applicability...
In this paper a method to develop artificial intuition is suggested. In an attempt to emulate the trial and error, searching is combined with a random choice. It is used in initial steps of the search, which provides reaching the goal in fewer steps, when compared to the case without the random choice. An example game is derived to illustrate the proposed searching technique.
Demand response (DR) programs are designed to reduce electricity load in periods of peak electricity demand, which helps avoiding expensive network upgrades. This reduction is measured by comparing the actual load with a baseline estimate. The baseline is a counter-factual load that could have been consumed in the absence of the DR program. Criteria for a good baseline methodology are accuracy, simplicity,...
A standard data set is useful to empirically evaluate classification rules learning algorithms. However, there is still no standard data set which is common enough for various situations. Data sets from the real world are limited to specific applications. The sizes of attributes, the rules and samples of the real data are fixed. A data generator is proposed here to produce synthetic data set which...
Massive proliferation of social media has opened possibilities for perpetrator to conduct the crime of online child grooming. Because the pervasiveness of the problem scale, it may only be tamed effectively and efficiently by using an automatic grooming conversation detection system. Previously, Pranoto, Gunawan, and Soewito [1] had developed a logistic model for the purpose and the model was able...
The MOOCs (Massive Open Online Courses) represent a category in the frame of TEL (Technology Enhanced Learning) particularly fashionable today since they allow the largest number of learners to access specific teachings. However, the principle of proposing very sequential and linear pedagogical paths is not attractive enough. In fact, the low success rate shows that it is necessary to maintain the...
In recent years, researches of aspect-category-based sentiment analysis have been approached in terms of predefined categories. In this paper, we target two sub-tasks of SemEval-2014 Task 4 dedicated to aspect-based sentiment analysis: detecting aspect category and aspect category polarity. Also, a pre-identified set of aspect categories {food, price, service, ambience, miscellaneous} defined by SemEval-2014...
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