7,450 research outputs found
Dynamic Covariance Models for Multivariate Financial Time Series
The accurate prediction of time-changing covariances is an important problem
in the modeling of multivariate financial data. However, some of the most
popular models suffer from a) overfitting problems and multiple local optima,
b) failure to capture shifts in market conditions and c) large computational
costs. To address these problems we introduce a novel dynamic model for
time-changing covariances. Over-fitting and local optima are avoided by
following a Bayesian approach instead of computing point estimates. Changes in
market conditions are captured by assuming a diffusion process in parameter
values, and finally computationally efficient and scalable inference is
performed using particle filters. Experiments with financial data show
excellent performance of the proposed method with respect to current standard
models
3D Human Activity Recognition with Reconfigurable Convolutional Neural Networks
Human activity understanding with 3D/depth sensors has received increasing
attention in multimedia processing and interactions. This work targets on
developing a novel deep model for automatic activity recognition from RGB-D
videos. We represent each human activity as an ensemble of cubic-like video
segments, and learn to discover the temporal structures for a category of
activities, i.e. how the activities to be decomposed in terms of
classification. Our model can be regarded as a structured deep architecture, as
it extends the convolutional neural networks (CNNs) by incorporating structure
alternatives. Specifically, we build the network consisting of 3D convolutions
and max-pooling operators over the video segments, and introduce the latent
variables in each convolutional layer manipulating the activation of neurons.
Our model thus advances existing approaches in two aspects: (i) it acts
directly on the raw inputs (grayscale-depth data) to conduct recognition
instead of relying on hand-crafted features, and (ii) the model structure can
be dynamically adjusted accounting for the temporal variations of human
activities, i.e. the network configuration is allowed to be partially activated
during inference. For model training, we propose an EM-type optimization method
that iteratively (i) discovers the latent structure by determining the
decomposed actions for each training example, and (ii) learns the network
parameters by using the back-propagation algorithm. Our approach is validated
in challenging scenarios, and outperforms state-of-the-art methods. A large
human activity database of RGB-D videos is presented in addition.Comment: This manuscript has 10 pages with 9 figures, and a preliminary
version was published in ACM MM'14 conferenc
Graphic Symbol Recognition using Graph Based Signature and Bayesian Network Classifier
We present a new approach for recognition of complex graphic symbols in
technical documents. Graphic symbol recognition is a well known challenge in
the field of document image analysis and is at heart of most graphic
recognition systems. Our method uses structural approach for symbol
representation and statistical classifier for symbol recognition. In our system
we represent symbols by their graph based signatures: a graphic symbol is
vectorized and is converted to an attributed relational graph, which is used
for computing a feature vector for the symbol. This signature corresponds to
geometry and topology of the symbol. We learn a Bayesian network to encode
joint probability distribution of symbol signatures and use it in a supervised
learning scenario for graphic symbol recognition. We have evaluated our method
on synthetically deformed and degraded images of pre-segmented 2D architectural
and electronic symbols from GREC databases and have obtained encouraging
recognition rates.Comment: 5 pages, 8 figures, Tenth International Conference on Document
Analysis and Recognition (ICDAR), IEEE Computer Society, 2009, volume 10,
1325-132
A method for exploratory repeated-measures analysis applied to a breast-cancer screening study
When a model may be fitted separately to each individual statistical unit,
inspection of the point estimates may help the statistician to understand
between-individual variability and to identify possible relationships. However,
some information will be lost in such an approach because estimation
uncertainty is disregarded. We present a comparative method for exploratory
repeated-measures analysis to complement the point estimates that was motivated
by and is demonstrated by analysis of data from the CADET II breast-cancer
screening study. The approach helped to flag up some unusual reader behavior,
to assess differences in performance, and to identify potential random-effects
models for further analysis.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS481 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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