8,731 research outputs found
Learning Interpretable Rules for Multi-label Classification
Multi-label classification (MLC) is a supervised learning problem in which,
contrary to standard multiclass classification, an instance can be associated
with several class labels simultaneously. In this chapter, we advocate a
rule-based approach to multi-label classification. Rule learning algorithms are
often employed when one is not only interested in accurate predictions, but
also requires an interpretable theory that can be understood, analyzed, and
qualitatively evaluated by domain experts. Ideally, by revealing patterns and
regularities contained in the data, a rule-based theory yields new insights in
the application domain. Recently, several authors have started to investigate
how rule-based models can be used for modeling multi-label data. Discussing
this task in detail, we highlight some of the problems that make rule learning
considerably more challenging for MLC than for conventional classification.
While mainly focusing on our own previous work, we also provide a short
overview of related work in this area.Comment: Preprint version. To appear in: Explainable and Interpretable Models
in Computer Vision and Machine Learning. The Springer Series on Challenges in
Machine Learning. Springer (2018). See
http://www.ke.tu-darmstadt.de/bibtex/publications/show/3077 for further
informatio
Model-Independent Pricing of Asian Options via Optimal Martingale Transport
In this article we discuss the problem of calculating optimal
model-independent (robust) bounds for the price of Asian options with discrete
and continuous averaging. We will give geometric characterisations of the
maximising and the minimising pricing model for certain types of Asian options
in discrete and continuous time. In discrete time the problem is reduced to
finding the optimal martingale transport for the cost function . In the
continuous time case we consider the cases with one and two given marginals. We
describe the maximising models in both of these cases as well as the minimising
model in the one-marginal case and relate the two-marginals case to the
discrete time problem with two marginals
How behavioral constraints may determine optimal sensory representations
The sensory-triggered activity of a neuron is typically characterized in
terms of a tuning curve, which describes the neuron's average response as a
function of a parameter that characterizes a physical stimulus. What determines
the shapes of tuning curves in a neuronal population? Previous theoretical
studies and related experiments suggest that many response characteristics of
sensory neurons are optimal for encoding stimulus-related information. This
notion, however, does not explain the two general types of tuning profiles that
are commonly observed: unimodal and monotonic. Here, I quantify the efficacy of
a set of tuning curves according to the possible downstream motor responses
that can be constructed from them. Curves that are optimal in this sense may
have monotonic or non-monotonic profiles, where the proportion of monotonic
curves and the optimal tuning curve width depend on the general properties of
the target downstream functions. This dependence explains intriguing features
of visual cells that are sensitive to binocular disparity and of neurons tuned
to echo delay in bats. The numerical results suggest that optimal sensory
tuning curves are shaped not only by stimulus statistics and signal-to-noise
properties, but also according to their impact on downstream neural circuits
and, ultimately, on behavior.Comment: 24 pages, 9 figures (main text + supporting information
A statistical reduced-reference method for color image quality assessment
Although color is a fundamental feature of human visual perception, it has
been largely unexplored in the reduced-reference (RR) image quality assessment
(IQA) schemes. In this paper, we propose a natural scene statistic (NSS)
method, which efficiently uses this information. It is based on the statistical
deviation between the steerable pyramid coefficients of the reference color
image and the degraded one. We propose and analyze the multivariate generalized
Gaussian distribution (MGGD) to model the underlying statistics. In order to
quantify the degradation, we develop and evaluate two measures based
respectively on the Geodesic distance between two MGGDs and on the closed-form
of the Kullback Leibler divergence. We performed an extensive evaluation of
both metrics in various color spaces (RGB, HSV, CIELAB and YCrCb) using the TID
2008 benchmark and the FRTV Phase I validation process. Experimental results
demonstrate the effectiveness of the proposed framework to achieve a good
consistency with human visual perception. Furthermore, the best configuration
is obtained with CIELAB color space associated to KLD deviation measure
- âŠ