Classification System for Mortgage Arrear Management
... Background The ING Domestic Bank possesses around 22% market share of Dutch mortgages. Normally, mortgage customers have to pay the interest or deposit monthly. But somehow, a considerable number of customers repay late, or default for one or even several months, which brings tremendous losses to IN ...
... Background The ING Domestic Bank possesses around 22% market share of Dutch mortgages. Normally, mortgage customers have to pay the interest or deposit monthly. But somehow, a considerable number of customers repay late, or default for one or even several months, which brings tremendous losses to IN ...
Quality scheme assessment in the clustering process
... partitioning of the specific data set based on a well defined quality index. In the following sections we elaborate on our approach. 3.1 Quality of Clustering Schemes The objective of the clustering methods is to provide optimal partitions of a data set. In general, they should search for clusters ...
... partitioning of the specific data set based on a well defined quality index. In the following sections we elaborate on our approach. 3.1 Quality of Clustering Schemes The objective of the clustering methods is to provide optimal partitions of a data set. In general, they should search for clusters ...
Towards Effective and Efficient Distributed Clustering
... why the data cannot be transmitted to a central site, e.g. limited bandwidth or security aspects. The transmission of huge amount of data from one site to another central site is in some application areas almost impossible. In astronomy, for instance, there exist several high sophisticated space tel ...
... why the data cannot be transmitted to a central site, e.g. limited bandwidth or security aspects. The transmission of huge amount of data from one site to another central site is in some application areas almost impossible. In astronomy, for instance, there exist several high sophisticated space tel ...
linear manifold correlation clustering
... The detection of correlations is a data mining task of increasing importance due to new areas of application such as DNA microarray analysis, collaborative filtering, and text mining. In these cases object similarity is no longer measured by physical distance, but rather by the behavior patterns obj ...
... The detection of correlations is a data mining task of increasing importance due to new areas of application such as DNA microarray analysis, collaborative filtering, and text mining. In these cases object similarity is no longer measured by physical distance, but rather by the behavior patterns obj ...
Fast Rank-2 Nonnegative Matrix Factorization for
... descent framework is applied to rank-2 NMF, each subproblem requires a solution for nonnegative least squares (NNLS) with only two columns. We design the algorithm for rank2 NMF by exploiting the fact that an exhaustive search for the optimal active set can be performed extremely fast when solving t ...
... descent framework is applied to rank-2 NMF, each subproblem requires a solution for nonnegative least squares (NNLS) with only two columns. We design the algorithm for rank2 NMF by exploiting the fact that an exhaustive search for the optimal active set can be performed extremely fast when solving t ...
A Dynamic Indexing Technique for Multidimensional Non-Ordered Discrete Data Spaces, ACM Transactions on Database Systems, Vol. 31, No. 2, 2006, Gang Qian, Qiang Zhu, Qiang Xue and Sakti Pramanik.
... category such as the R-tree [Guttman 1984], the R*-tree [Beckmann et al. 1990], the SS-tree [White and Jain 1996], the SR-tree [Katayama and Satoh 1997] and the X-tree [Berchtold et al. 1996] split an overflow node by partitioning the set of its indexed data objects (according to their distribution) ...
... category such as the R-tree [Guttman 1984], the R*-tree [Beckmann et al. 1990], the SS-tree [White and Jain 1996], the SR-tree [Katayama and Satoh 1997] and the X-tree [Berchtold et al. 1996] split an overflow node by partitioning the set of its indexed data objects (according to their distribution) ...
The Craft of Economic Modeling
... how well it works forecasting a few quarters ahead, but by how well it holds up over a much longer period. Can it keep employment within a few percent of the labor force over decades? Can it keep inflation in line with the increase in money supply though it does not use money supply in the inflation ...
... how well it works forecasting a few quarters ahead, but by how well it holds up over a much longer period. Can it keep employment within a few percent of the labor force over decades? Can it keep inflation in line with the increase in money supply though it does not use money supply in the inflation ...
Adaptive Intrusion Detection based on Boosting and
... the training examples updated according to how they were classified. If a training example is misclassified then its weight is increased, or if correctly classified then its weight is decreased. Then the algorithm creates another new data set with the misclassification error produced by each trainin ...
... the training examples updated according to how they were classified. If a training example is misclassified then its weight is increased, or if correctly classified then its weight is decreased. Then the algorithm creates another new data set with the misclassification error produced by each trainin ...
Expectation–maximization algorithm
In statistics, an expectation–maximization (EM) algorithm is an iterative method for finding maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. These parameter-estimates are then used to determine the distribution of the latent variables in the next E step.