116 research outputs found
Generating High-Order Threshold Functions with Multiple Thresholds
In this paper, we consider situations in which a given logical function is
realized by a multithreshold threshold function. In such situations, constant
functions can be easily obtained from multithreshold threshold functions, and
therefore, we can show that it becomes possible to optimize a class of
high-order neural networks. We begin by proposing a generating method for
threshold functions in which we use a vector that determines the boundary
between the linearly separable function and the high-order threshold function.
By applying this method to high-order threshold functions, we show that
functions with the same weight as, but a different threshold than, a threshold
function generated by the generation process can be easily obtained. We also
show that the order of the entire network can be extended while maintaining the
structure of given functions.Comment: 7 page
Retinal Vessel Segmentation Using the 2-D Morlet Wavelet and Supervised Classification
We present a method for automated segmentation of the vasculature in retinal
images. The method produces segmentations by classifying each image pixel as
vessel or non-vessel, based on the pixel's feature vector. Feature vectors are
composed of the pixel's intensity and continuous two-dimensional Morlet wavelet
transform responses taken at multiple scales. The Morlet wavelet is capable of
tuning to specific frequencies, thus allowing noise filtering and vessel
enhancement in a single step. We use a Bayesian classifier with
class-conditional probability density functions (likelihoods) described as
Gaussian mixtures, yielding a fast classification, while being able to model
complex decision surfaces and compare its performance with the linear minimum
squared error classifier. The probability distributions are estimated based on
a training set of labeled pixels obtained from manual segmentations. The
method's performance is evaluated on publicly available DRIVE and STARE
databases of manually labeled non-mydriatic images. On the DRIVE database, it
achieves an area under the receiver operating characteristic (ROC) curve of
0.9598, being slightly superior than that presented by the method of Staal et
al.Comment: 9 pages, 7 figures and 1 table. Accepted for publication in IEEE
Trans Med Imag; added copyright notic
Extreme Entropy Machines: Robust information theoretic classification
Most of the existing classification methods are aimed at minimization of
empirical risk (through some simple point-based error measured with loss
function) with added regularization. We propose to approach this problem in a
more information theoretic way by investigating applicability of entropy
measures as a classification model objective function. We focus on quadratic
Renyi's entropy and connected Cauchy-Schwarz Divergence which leads to the
construction of Extreme Entropy Machines (EEM).
The main contribution of this paper is proposing a model based on the
information theoretic concepts which on the one hand shows new, entropic
perspective on known linear classifiers and on the other leads to a
construction of very robust method competetitive with the state of the art
non-information theoretic ones (including Support Vector Machines and Extreme
Learning Machines).
Evaluation on numerous problems spanning from small, simple ones from UCI
repository to the large (hundreads of thousands of samples) extremely
unbalanced (up to 100:1 classes' ratios) datasets shows wide applicability of
the EEM in real life problems and that it scales well
Extreme entropy machines : robust information theoretic classification
Most existing classification methods are aimed
at minimization of empirical risk (through some simple
point-based error measured with loss function) with added
regularization. We propose to approach the classification
problem by applying entropy measures as a model objective
function. We focus on quadratic Renyi’s entropy and
connected Cauchy-Schwarz Divergence which leads to the
construction of extreme entropy machines (EEM). The
main contribution of this paper is proposing a model based
on the information theoretic concepts which on the one
hand shows new, entropic perspective on known linear
classifiers and on the other leads to a construction of very
robust method competitive with the state of the art noninformation
theoretic ones (including Support Vector
Machines and Extreme Learning Machines). Evaluation on
numerous problems spanning from small, simple ones from
UCI repository to the large (hundreds of thousands of
samples) extremely unbalanced (up to 100:1 classes’
ratios) datasets shows wide applicability of the EEM in
real-life problems. Furthermore, it scales better than all
considered competitive methods
Multithreshold Segmentation by Using an Algorithm Based on the Behavior of Locust Swarms
As an alternative to
classical techniques, the problem of image
segmentation has also been handled through
evolutionary methods. Recently, several
algorithms based on evolutionary principles have
been successfully applied to image segmentation
with interesting performances. However, most of
them maintain two important limitations: (1)
they frequently obtain suboptimal results
(misclassifications) as a consequence of an
inappropriate balance between exploration and
exploitation in their search strategies; (2) the
number of classes is fixed and known in advance.
This paper presents an algorithm for the
automatic selection of pixel classes for image
segmentation. The proposed method combines a
novel evolutionary method with the definition of
a new objective function that appropriately
evaluates the segmentation quality with respect
to the number of classes. The new evolutionary
algorithm, called Locust Search (LS), is based
on the behavior of swarms of locusts. Different
to the most of existent evolutionary algorithms,
it explicitly avoids the concentration of
individuals in the best positions, avoiding
critical flaws such as the premature convergence
to suboptimal solutions and the limited
exploration-exploitation balance. Experimental
tests over several benchmark functions and
images validate the efficiency of the proposed
technique with regard to accuracy and
robustness
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