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Academy of Mathematics and Systems Science, CAS Colloquia & Seminars：Sparse Tensor Optimization Approaches for Traffic Flow Prediction, Anomaly Detection and Video Surveillance
交通流预测 异常检测 视频监控 稀疏张量 优化方法
2023/4/13
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...
Integrative approaches for microRNA target prediction: combining sequence information and the paired mRNA and miRNA expression profiles
miRNA target prediction expression profile integrative analysis
2016/1/25
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...
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
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 ...
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 ...
BOOSTING ALGORITHMS:REGULARIZATION,PREDICTION AND MODEL FITTING
Generalized linear models Generalized additive models Gradient boosting Survival analysis Variable selection Software
2015/8/21
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...
Comment:Boosting Algorithms:Regularization,Prediction and Model Fitting
Boosting Algorithms Regularization Prediction Model Fitting
2015/8/21
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-...
Empirical Bayes Estimates for Large-Scale Prediction Problems
microarray prediction empirical Bayes shrunken centroids
2015/8/20
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
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
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...
Series Prediction based on Algebraic Approximants
Hermite-Pade polynomial Algebraic approximant Pade approximant prediction of coefficients
2011/9/5
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...
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 ...
Scaling laws prediction from a solvable model of turbulent thermal convection
Scaling laws solvable model turbulent thermal convection
2011/7/7
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...
The standard model of online prediction deals with serial processing of inputs by a single
processor.
Optimal Distributed Online Prediction using Mini-Batches
distributed learning online learning stochastic optimization
2011/3/2
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...