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Tensor-based modeling and computation emerge prominently with urgent demands from practical applications in the big data era. With the intrinsic sparsity in real data sets and the dimensionality reduc...
Gene regulation is a key factor in gaining a full understanding of molecular biology. microRNA (miRNA), a novel class of non-coding RNA, has recently been found to be one crucial class of post-transac...
Theory-Based Prediction of Early Reading     Early Reading  Theory-Based       font style='font-size:12px;'> 2015/9/28
This article presents a theory of the cognitive processes involved in learning to read and examines the degree to which measures derived from this theory are able to predict success in reading. Measur...
Prediction by Supervised Principal Components     Gene expression  Microarray  Regression  Survival analysis       font style='font-size:12px;'> 2015/8/21
In regression problems where the number of predictors greatly exceeds the number of observations, conventional regression techniques may produce unsatisfactory results. We describe a technique called ...
Prediction by supervised principal components     Prediction  supervised  principal components       font style='font-size:12px;'> 2015/8/21
In regression problems where the number of predictors greatly exceeds the number of observations, conventional regression techniques may produce unsatisfactory results. We describe a technique called ...
We present a statistical perspective on boosting. Special emphasis is given to estimating potentially complex parametric or nonparametric models, including generalized linear and additive models as we...
We congratulate the authors (hereafter BH) for an interesting take on the boosting technology, and for developing a modular computational environment in R for exploring their models. Their use of low-...
Classical prediction methods such as Fisher's linear discriminant function were designed for small-scale problems, where the number of predictors N is much smaller than the number of observations n....
Link Prediction in Graphs with Autoregressive Features     Autoregressive Features  Link Prediction  Graphs       font style='font-size:12px;'> 2012/11/22
In the paper, we consider the problem of link prediction in time-evolving graphs. We assume that certain graph features, such as the node degree, follow a vector autoregressive (VAR) model and we prop...
On spatial selectivity and prediction across conditions with fMRI     Machine learning  fMRI  feature selection  regions       font style='font-size:12px;'> 2012/11/22
Researchers in functional neuroimaging mostly use activation coordinates to formulate their hypotheses. Instead, we propose to use the full statistical images to define regions of interest (ROIs). Thi...
Abstract: It is described how the Hermite-Pad\'e polynomials corresponding to an algebraic approximant for a power series may be used to predict coefficients of the power series that have not been use...
Prediction and Modularity in Dynamical Systems     Dynamical Systems  Prediction  Modularity       font style='font-size:12px;'> 2011/7/6
Identifying and understanding modular organizations is centrally important in the study of complex systems. Several approaches to this problem have been advanced, many framed in information-theoretic ...
A solvable turbulent model is used to predict both the structure of the boundary layer and the scaling laws in thermal convection. The transport of heat depends on the interplay between the thermal, v...
Robust Distributed Online Prediction      Robust Distributed  Online Prediction        font style='font-size:12px;'> 2011/3/2
The standard model of online prediction deals with serial processing of inputs by a single processor.
Online prediction methods are typically presented as serial algorithms running on a single processor. However, in the age of web-scale prediction problems, it is increasingly common to encounter sit...

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