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On an average 9 out of 10 startups fail(industry standard). Several reasons are responsible for the failure of a startup including bad management, lack of funds, etc. This work aims to create a predictive model for startups based on many key things involved at various stages in the life of a startup. It is highly desirable to increase the success rate of startups and not much work have been done to...
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...
With the proliferation of diversified social network services, understanding how the influence is propagated helps us better understand the network evolution mechanism and the social impact of different kinds of information. Existing models are mostly built on the static network structure. They fail to catch the temporal dynamic property of social network. In this paper, we design a new kind of latent...
The future power grid will need to incorporate systems and processes with a higher degree of variability and randomness due to the penetration of renewable energy resources and the increase of energy demand. Forecasting variables in a more uncertain environment poses new challenges and revisions of the existing forecasting methodologies will have to be made to maintain forecasting accuracy. This paper...
Software fault prediction improves software qualityand testing efficiency by early identification of faults. Classification models using code attributes are constructed and used for prediction. This paper is a study of software fault prediction using Multi-Layered Perceptron, Bayesian Network and Naive Bayes classifier and their comparison by showing predictive and comprehensible performance. A framework...
Personalization and adaptation are at the core of Intelligent Tutoring Systems. The Bayesian Knowledge Tracing (BKT) Student Model is a time-tested method that maintains information about students' knowledge levels for the different skills in the topic domain. In our previous work, we had proposed the Personalized, Clustered, Bayesian Knowledge Tracing (PC-BKT) model that individualizes the learning...
Low-rank matrix completion methods have been successful in a variety of settings such as recommendation systems. However, most of the existing matrix completion methods only provide a point estimate of missing entries, and do not characterize uncertainties of the predictions. In this paper, we propose a Bayesian hierarchical probabilistic matrix factorization (BHPMF) model to 1) incorporate hierarchical...
A Peer-to-Peer (P2P) traffic identification method based on Bayesian trust sampling is presented in this paper, which predicts the fluctuation degree for next cycle of P2P traffic ratio, and optimizes for the used amount of historical proportion estimation. Simulation results show that, under the premise of using a fixed number of the estimated values for historical P2P ratio, this trust method makes...
An Intelligent Tutoring System (ITS) supplements traditional learning methods and is used for personalized learning purposes that range from exploring simple examples to understanding intricate problems. The Bayesian Knowledge Tracing (BKT) model is an established method for student modeling. A recent enhancement to the BKT model is the BKT-PPS (Prior Per Student) which introduces a prior learnt for...
Cloud SLAs are contractually binding agreements between cloud service providers and cloud consumers. For cloud service providers, it is essential to prevent SLA violations as much as possible to enhance customer satisfaction and avoid penalty payments. Therefore, it is desirable for providers to predict possible violations before they happen. We propose an approach for predicting SLA violations, which...
User's personal social networks are big and cluttered, yet contain highly valuable information. Organizing users' friends into circles or communities is a fundamental task in social network research. Social network sites allow users to manually categorize their friends into social circles, however this process is laborious and inadaptable to changes. In this paper, we study novel ways of automatically...
Multivariate Pattern Analysis (MVPA) is frequently used to decode cognitive states from brain activities in fMRI study. Due to the discrepancy between sample and feature size, MVPA methods are suffered from the overfitting problem. This paper addresses this issue by introducing sparse modelling along with its advanced decoding method, Compressive Sensing (CS). As brain voxels have highly correlated...
In social networks, predicting a user's locations through those of his or her friends mainly relies on the selection method of the most influential friends of the user, which most of the existing location prediction methods fail to attach importance to. In this paper, we firstly present an analytical procedure in regard to the calculation of the theoretical maximum accuracy for location prediction...
Software prediction unveils itself as a difficult but important task which can aid the manager on decision making, possibly allowing for time and resources sparing, achieving higher software quality among other benefits. Bayesian Networks are one of the machine learning techniques proposed to perform this task. However, the data pre-processing procedures related to their application remain scarcely...
This paper introduces a Bayesian model to predict and classify the mobility of a node in Mobile Ad-hoc Networks (MANETs). The proposed model does not use the additional information from Global Positioning System (GPS) for its prediction as some existing models did. Instead, it relies on the “average encounter rate” and “node degree” calculated at each node. However, the outcome is still recorded at...
There are many quantitative estimation methods, e.g. linear regression, neural networks, regression trees. Compared to traditional methods, Bayesian networks are being increasingly used in software engineering because their use opens many possibilities. A main feature of Bayesian networks is their capability to combine data and expert knowledge. This paper seeks to reinforce the hypothesis that Bayesian...
The county level of basic public services analysis and classification play an important role in county economic growth and improve benefit of healthy development of urbanization in China. According to the county level of basic public services data which is large scale and imbalance, this paper presented a support vector machine model to classify the county level of basic public services. The method...
City innovative capability analysis and prediction play an important role in regional innovation systems development and improve benefit of innovative capability for country. According to the city innovative capability data which is large scale and imbalance, this paper presented a support vector machine model to predict city innovative capability. The method was compared with artificial neural network,...
Forecasting applications on the stock market attract much interest from researchers in the artificial intelligence field. The problem tackled in this study concerns predicting the direction of change of stock price indices, formulated in terms of binary classification. We use gene expression programming to evolve pools of binary classifiers and investigate several approaches to construct ensembles...
Financial distress and bankruptcy of companies may cause the resources to be wasted and the investment opportunities to be faded. Bankruptcy prediction by providing necessary warnings can make the companies aware of this problem so they can take appropriate measures with these warnings. The aim of this study is model development for financial distress prediction of listed companies in Tehran stocks...
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