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Link prediction is an important issue in Social Network Analysis area. Most of the existing link prediction methods aim to find the missing links or to predict the future links mainly based on a static network, ignoring the evolution of the network over time. This paper proposes a link prediction method that can learn from network dynamics. Using machine learning techniques, the method models the...
This paper presents a DBN (deep belief nets) model and a multi-modality feature extraction method to extend features' dimensionalities of short text for Chinese micro blogging sentiment classification. Besides traditional features sets for document classification, comments for certain posts are also extracted as part of the micro blogging features according to the relationship between commenters and...
Distance education has evolved significantly in last few years. Many educational organizations have adopted this approach, and for some of them, it has been the only format offered. Although, distance education is often compared with traditional education there are issues to be solved. Distance education has been also used for employees training in software development organization. A challenge is...
For admissions in various graduate schools and colleges. There are some mobile applications available for GRE preparation. Those applications consists of only words with their corresponding synonym which are not very effective as they to lack to provide tricks for easy remembrance of the GRE words and there is no specialized categorization of the words and they neither provide any interesting verbal...
Threaded debate forums have become one of the major social media platforms. Usually people argue with one another using not only claims and evidences about the topic under discussion but also language used to organize them, which we refer to as shell. In this paper, we study how to separate shell from topical contents using unsupervised methods. Along this line, we develop a latent variable model...
The current trend of growth of information reveals that it is inevitable that large-scale learning problems become the norm. In this paper, we propose and analyze a novel Low-density Cut based tree Decomposition method for large-scale SVM problems, called LCD-SVM. The basic idea here is divide and conquer: use a decision tree to decompose the data space and train SVMs on the decomposed regions. Specifically,...
This paper presents iNNE (isolation using Nearest Neighbour Ensemble), an efficient nearest neighbour-based anomaly detection method by isolation. Inne runs significantly faster than existing nearest neighbour-based methods such as Local Outlier Factor, especially in data sets having thousands of dimensions or millions of instances. This is because the proposed method has linear time complexity and...
As a derivative of Restricted Boltzmann Machine (RBM), classification RBM (Class RBM) is proved to be an effective classifier with a probabilistic interpretation. Several elegant learning methods/models related to Class RBM have been proposed. This paper proposes and analyzes a Rényi divergence based generalization for discriminative learning objective of Class RBM. Specifically, we extend the Conditional...
The traditional k-NN classification rule predicts a label based on the most common label of the k nearest neighbors (the plurality rule). It is known that the plurality rule is optimal when the number of examples tends to infinity. In this paper we show that the plurality rule is sub-optimal when the number of labels is large and the number of examples is small. We propose a simple k-NN rule that...
Bayesian network structures are usually built using only the data and starting from an empty network or from a naive Bayes structure. Very often, in some domains, like medicine, a prior structure is already known based on expert knowledge. This structure can be automatically or manually refined in search for better performance models. In this work, we take Bayesian networks built by specialists and...
This work presents a study on prediction of university enrollment using three computational intelligence (CI) techniques. The enrollment forecasting has been considered as a form of time series prediction using CI techniques that include an artificial neural network (ANN), a neuro-fuzzy inference system (ANFIS) and an aggregated fuzzy time series model. A novel form of ANN, namely, single multiplicative...
Addiction or Substance Use Disorders (SUD) is a growing public health problem in India. There are very few trained health professionals to provide evidence based care for these conditions. We initiated a weekly tele-ECHO clinic to train health professionals in the recognition and management of Alcohol and tobacco use disorders. The preliminary results points towards the feasibility as well as acceptability...
Effective machine-learning handles large datasets efficiently. One key feature of handling large data is the use of databases such as MySQL. The freeware fuzzy decision tree induction tool, FDT, is a scalable supervised-classification software tool implementing fuzzy decision trees. It is based on an optimized fuzzy ID3 (FID3) algorithm. FDT 2.0 improves upon FDT 1.0 by bridging the gap between data...
In order to utilize identification to the best extent, we need robust and fast algorithms and systems to process the data. Having palmprint as a reliable and unique characteristic of every person, we extract and use its features based on its geometry, lines and angles. There are countless ways to define measures for the recognition task. To analyze a new point of view, we extracted textural features...
A major issue faced by the education system in highly populated countries like India is the shortage of experienced and trained teachers and trainers. In order to alleviate this issue, we describe how the e-Learning tool AVIEW is being used for large-scale online training and teaching. A-VIEW allows thousands of participants at various geographical locations to be trained and taught at the same time...
Selecting good stocks for investment is an essential problem in finance. However, accessing stocks is very difficult because many factors may affect the stocks and their relationship is also very complicated to analyze. Applying probabilistic statistical classification model, such as logistic regression, for stock analysis is promising. Logistic regression is an efficient classifier for predicting...
This study presents the utilization of a Hybrid Education Platform for the realization of a versatile blended learning model oriented to computer engineering and science educators. Furthermore, a data mining approach is introduced to analyze the questionnaires that learners submit concerning the learning activities they have participated in. Specifically, a clustering and classification methodology...
Identifying bad objects hidden amidst many good objects is important for public safety and decision-making. These problems are complicated in that the cost of leaving a bad object unidentified may not be specified easily, making it difficult to apply existing cost-sensitive classification that depends on knowing a cost matrix or cost distribution. A compelling case for this "illusive cost"...
Graph classification has traditionally focused on graphs generated from a single feature view. In many applications, it is common to have useful information from different channels/views to describe objects, which naturally results in a new representation with multiple graphs generated from different feature views being used to describe one object. In this paper, we formulate a new Multi-Graph-View...
Paper analyzes the possibilities of didactic use of simulators in teaching the various subjects involved in medicine, health sciences and in particular case of Biomedical Engineering. Describes the theory of didactic simulation, shows some important international developments and especially various devices and systems developed at the School of Biomedical Engineering, University of Valparaiso in Chile...
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