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Financial information extraction from big financial reports is a tedious task. This paper speaks about page-wise feature generation and applying learning algorithms for identifying financial information (balance sheets, cash flows, and income statements) in Form 10-K or annual reports of companies. Balance sheets, cash flows, and income statements have some structure in them and are semi-structured...
In this paper, we present correlated logistic (CorrLog) model for multilabel image classification. CorrLog extends conventional logistic regression model into multilabel cases, via explicitly modeling the pairwise correlation between labels. In addition, we propose to learn the model parameters of CorrLog with elastic net regularization, which helps exploit the sparsity in feature selection and label...
Regularized logistic regression models have recently become an important classification tool for high dimensional problems due to their sparseness and embedded feature selection property of the ℓ1 penalty. However, the degree of sparseness is determined by a regularization parameter λ, whose selection is typically done by cross validation. In this paper we study the applicability of a recently proposed...
Based on the forefathers' research, this paper made an empirical study on reason of financial distress. We chose the panel data of new ST companies between 2004 and 2006 and used the method of Logistic regression to find the result. The empirical results indicated that not all the countermeasures we chosen are effective. At last, we got our conclusion that the indicators of current ratio, cash flow...
Four-parameter logistic model is used to describe height-diameter relationship of dahurian larch (Larix gmelinii. Rupr.) from longitudinal measurements using nonlinear mixed-effects modeling approach. The parameter variation in the model was divided into random effects, fixed effects, and variance-covariance structure. The values for fixed effects parameters and the variance-covariance matrix of random...
Estimating containership arrival rate is a key element in harbor operation and management; however, it is not easy to be described because of a wide range of external factors. Most of the literature discussing arrival processes is based on a homogeneous Poisson process, which is unable to describe the fluctuation status of growth or recession. In the paper, we propose the Non-Homogeneous Poisson Process...
Managing customer credit is an important issue in the banking industry and should always be done in an automatic way, with credit scoring trusted. This paper presents our solution to PAKDD 2009 data mining competition as a case study of the credit scoring problem. Following a brief description of the data mining task, several challenges confronted in the task such as imbalanced dataset, missing values...
Statistical models in which both fixed and random effects enter nonlinearly are becoming increasingly popular. These models have a wide variety of applications in many areas such as agriculture, forestry, biology, ecology, biomedicine, sociology, economics, pharmacokinetics, and other areas. Mixed effect models are flexible models to analyze grouped data including longitudinal data, repeated measures...
Nonlinear mixed-effects modeling approach was used to model the individual tree height-age relationship in Mongolian pine (Pinus sylvestris L.var.mongolica Litv.). A set of 345 pairs of height-age measurements was used to fit the model. These were taken at 30 temporary plots from natural stands. Ten nonlinear growth equations were evaluated to find a local model, which only includes the ages of the...
In this paper we present a new method of compensating for complex multipath channel fading in blind multiuser detection for DS-CDMA communication. This compensation allows for the use of different chip pulse shapes. The compensation is based on the incorporation of priors derived from the FFT of the pulse shape. The system employs detectors that incorporate methods of independent component analysis...
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