12,986 research outputs found
Optimal Control of Brownian Inventory Models with Convex Inventory Cost: Discounted Cost Case
We consider an inventory system in which inventory level fluctuates as a
Brownian motion in the absence of control. The inventory continuously
accumulates cost at a rate that is a general convex function of the inventory
level, which can be negative when there is a backlog. At any time, the
inventory level can be adjusted by a positive or negative amount, which incurs
a fixed positive cost and a proportional cost. The challenge is to find an
adjustment policy that balances the inventory cost and adjustment cost to
minimize the expected total discounted cost. We provide a tutorial on using a
three-step lower-bound approach to solving the optimal control problem under a
discounted cost criterion. In addition, we prove that a four-parameter control
band policy is optimal among all feasible policies. A key step is the
constructive proof of the existence of a unique solution to the free boundary
problem. The proof leads naturally to an algorithm to compute the four
parameters of the optimal control band policy
Optimal Bayes Classifiers for Functional Data and Density Ratios
Bayes classifiers for functional data pose a challenge. This is because
probability density functions do not exist for functional data. As a
consequence, the classical Bayes classifier using density quotients needs to be
modified. We propose to use density ratios of projections on a sequence of
eigenfunctions that are common to the groups to be classified. The density
ratios can then be factored into density ratios of individual functional
principal components whence the classification problem is reduced to a sequence
of nonparametric one-dimensional density estimates. This is an extension to
functional data of some of the very earliest nonparametric Bayes classifiers
that were based on simple density ratios in the one-dimensional case. By means
of the factorization of the density quotients the curse of dimensionality that
would otherwise severely affect Bayes classifiers for functional data can be
avoided. We demonstrate that in the case of Gaussian functional data, the
proposed functional Bayes classifier reduces to a functional version of the
classical quadratic discriminant. A study of the asymptotic behavior of the
proposed classifiers in the large sample limit shows that under certain
conditions the misclassification rate converges to zero, a phenomenon that has
been referred to as "perfect classification". The proposed classifiers also
perform favorably in finite sample applications, as we demonstrate in
comparisons with other functional classifiers in simulations and various data
applications, including wine spectral data, functional magnetic resonance
imaging (fMRI) data for attention deficit hyperactivity disorder (ADHD)
patients, and yeast gene expression data
Online Deception Detection Refueled by Real World Data Collection
The lack of large realistic datasets presents a bottleneck in online
deception detection studies. In this paper, we apply a data collection method
based on social network analysis to quickly identify high-quality deceptive and
truthful online reviews from Amazon. The dataset contains more than 10,000
deceptive reviews and is diverse in product domains and reviewers. Using this
dataset, we explore effective general features for online deception detection
that perform well across domains. We demonstrate that with generalized features
- advertising speak and writing complexity scores - deception detection
performance can be further improved by adding additional deceptive reviews from
assorted domains in training. Finally, reviewer level evaluation gives an
interesting insight into different deceptive reviewers' writing styles.Comment: 10 pages, Accepted to Recent Advances in Natural Language Processing
(RANLP) 201
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