10,667 research outputs found
Self-Configuring and Evolving Fuzzy Image Thresholding
Every segmentation algorithm has parameters that need to be adjusted in order
to achieve good results. Evolving fuzzy systems for adjustment of segmentation
parameters have been proposed recently (Evolving fuzzy image segmentation --
EFIS [1]. However, similar to any other algorithm, EFIS too suffers from a few
limitations when used in practice. As a major drawback, EFIS depends on
detection of the object of interest for feature calculation, a task that is
highly application-dependent. In this paper, a new version of EFIS is proposed
to overcome these limitations. The new EFIS, called self-configuring EFIS
(SC-EFIS), uses available training data to auto-configure the parameters that
are fixed in EFIS. As well, the proposed SC-EFIS relies on a feature selection
process that does not require the detection of a region of interest (ROI).Comment: To appear in proceedings of The 14th International Conference on
Machine Learning and Applications (IEEE ICMLA 2015), Miami, Florida, USA,
201
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Artificial Immune Systems - Models, algorithms and applications
Copyright © 2010 Academic Research Publishing Agency.This article has been made available through the Brunel Open Access Publishing Fund.Artificial Immune Systems (AIS) are computational paradigms that belong to the computational intelligence family and are inspired by the biological immune system. During the past decade, they have attracted a lot of interest from researchers aiming to develop immune-based models and techniques to solve complex computational or engineering problems. This work presents a survey of existing AIS models and algorithms with a focus on the last five years.This article is available through the Brunel Open Access Publishing Fun
Laplacian Mixture Modeling for Network Analysis and Unsupervised Learning on Graphs
Laplacian mixture models identify overlapping regions of influence in
unlabeled graph and network data in a scalable and computationally efficient
way, yielding useful low-dimensional representations. By combining Laplacian
eigenspace and finite mixture modeling methods, they provide probabilistic or
fuzzy dimensionality reductions or domain decompositions for a variety of input
data types, including mixture distributions, feature vectors, and graphs or
networks. Provable optimal recovery using the algorithm is analytically shown
for a nontrivial class of cluster graphs. Heuristic approximations for scalable
high-performance implementations are described and empirically tested.
Connections to PageRank and community detection in network analysis demonstrate
the wide applicability of this approach. The origins of fuzzy spectral methods,
beginning with generalized heat or diffusion equations in physics, are reviewed
and summarized. Comparisons to other dimensionality reduction and clustering
methods for challenging unsupervised machine learning problems are also
discussed.Comment: 13 figures, 35 reference
JUNIPR: a Framework for Unsupervised Machine Learning in Particle Physics
In applications of machine learning to particle physics, a persistent
challenge is how to go beyond discrimination to learn about the underlying
physics. To this end, a powerful tool would be a framework for unsupervised
learning, where the machine learns the intricate high-dimensional contours of
the data upon which it is trained, without reference to pre-established labels.
In order to approach such a complex task, an unsupervised network must be
structured intelligently, based on a qualitative understanding of the data. In
this paper, we scaffold the neural network's architecture around a
leading-order model of the physics underlying the data. In addition to making
unsupervised learning tractable, this design actually alleviates existing
tensions between performance and interpretability. We call the framework
JUNIPR: "Jets from UNsupervised Interpretable PRobabilistic models". In this
approach, the set of particle momenta composing a jet are clustered into a
binary tree that the neural network examines sequentially. Training is
unsupervised and unrestricted: the network could decide that the data bears
little correspondence to the chosen tree structure. However, when there is a
correspondence, the network's output along the tree has a direct physical
interpretation. JUNIPR models can perform discrimination tasks, through the
statistically optimal likelihood-ratio test, and they permit visualizations of
discrimination power at each branching in a jet's tree. Additionally, JUNIPR
models provide a probability distribution from which events can be drawn,
providing a data-driven Monte Carlo generator. As a third application, JUNIPR
models can reweight events from one (e.g. simulated) data set to agree with
distributions from another (e.g. experimental) data set.Comment: 37 pages, 24 figure
Machine Learning for Fluid Mechanics
The field of fluid mechanics is rapidly advancing, driven by unprecedented
volumes of data from field measurements, experiments and large-scale
simulations at multiple spatiotemporal scales. Machine learning offers a wealth
of techniques to extract information from data that could be translated into
knowledge about the underlying fluid mechanics. Moreover, machine learning
algorithms can augment domain knowledge and automate tasks related to flow
control and optimization. This article presents an overview of past history,
current developments, and emerging opportunities of machine learning for fluid
mechanics. It outlines fundamental machine learning methodologies and discusses
their uses for understanding, modeling, optimizing, and controlling fluid
flows. The strengths and limitations of these methods are addressed from the
perspective of scientific inquiry that considers data as an inherent part of
modeling, experimentation, and simulation. Machine learning provides a powerful
information processing framework that can enrich, and possibly even transform,
current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202
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