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13,202 | Tracking Tensor Subspaces with Informative Random Sampling for Real-Time
MR Imaging | cs.LG | Magnetic resonance imaging (MRI) nowadays serves as an important modality for
diagnostic and therapeutic guidance in clinics. However, the {\it slow
acquisition} process, the dynamic deformation of organs, as well as the need
for {\it real-time} reconstruction, pose major challenges toward obtaining
artifact-free image... | computer science |
13,203 | Local nearest neighbour classification with applications to
semi-supervised learning | math.ST | We derive a new asymptotic expansion for the global excess risk of a local
$k$-nearest neighbour classifier, where the choice of $k$ may depend upon the
test point. This expansion elucidates conditions under which the dominant
contribution to the excess risk comes from the locus of points at which each
class label is e... | computer science |
13,204 | Phase recovery and holographic image reconstruction using deep learning
in neural networks | cs.CV | Phase recovery from intensity-only measurements forms the heart of coherent
imaging techniques and holography. Here we demonstrate that a neural network
can learn to perform phase recovery and holographic image reconstruction after
appropriate training. This deep learning-based approach provides an entirely
new framewo... | computer science |
13,205 | Principal Component Analysis with Tensor Train Subspace | cs.LG | Tensor train is a hierarchical tensor network structure that helps alleviate
the curse of dimensionality by parameterizing large-scale multidimensional data
via a set of network of low-rank tensors. Associated with such a construction
is a notion of Tensor Train subspace and in this paper we propose a TT-PCA
algorithm ... | computer science |
13,206 | A Probabilistic Perspective on Gaussian Filtering and Smoothing | stat.ME | We present a general probabilistic perspective on Gaussian filtering and
smoothing. This allows us to show that common approaches to Gaussian
filtering/smoothing can be distinguished solely by their methods of
computing/approximating the means and covariances of joint probabilities. This
implies that novel filters and ... | computer science |
13,207 | On Maximum a Posteriori Estimation of Hidden Markov Processes | cs.AI | We present a theoretical analysis of Maximum a Posteriori (MAP) sequence
estimation for binary symmetric hidden Markov processes. We reduce the MAP
estimation to the energy minimization of an appropriately defined Ising spin
model, and focus on the performance of MAP as characterized by its accuracy and
the number of s... | computer science |
13,208 | Clustering by compression | cs.CV | We present a new method for clustering based on compression. The method
doesn't use subject-specific features or background knowledge, and works as
follows: First, we determine a universal similarity distance, the normalized
compression distance or NCD, computed from the lengths of compressed data files
(singly and in ... | computer science |
13,209 | Field geology with a wearable computer: 1st results of the Cyborg
Astrobiologist System | cs.CV | We present results from the first geological field tests of the `Cyborg
Astrobiologist', which is a wearable computer and video camcorder system that
we are using to test and train a computer-vision system towards having some of
the autonomous decision-making capabilities of a field-geologist. The Cyborg
Astrobiologist... | computer science |
13,210 | Efficient Adaptive Compressive Sensing Using Sparse Hierarchical Learned
Dictionaries | stat.ML | Recent breakthrough results in compressed sensing (CS) have established that
many high dimensional objects can be accurately recovered from a relatively
small number of non- adaptive linear projection observations, provided that the
objects possess a sparse representation in some basis. Subsequent efforts have
shown th... | computer science |
13,211 | Speckle Reduction in Polarimetric SAR Imagery with Stochastic Distances
and Nonlocal Means | cs.IT | This paper presents a technique for reducing speckle in Polarimetric
Synthetic Aperture Radar (PolSAR) imagery using Nonlocal Means and a
statistical test based on stochastic divergences. The main objective is to
select homogeneous pixels in the filtering area through statistical tests
between distributions. This propo... | computer science |
13,212 | SAR Image Despeckling Algorithms using Stochastic Distances and Nonlocal
Means | cs.IT | This paper presents two approaches for filter design based on stochastic
distances for intensity speckle reduction. A window is defined around each
pixel, overlapping samples are compared and only those which pass a
goodness-of-fit test are used to compute the filtered value. The tests stem
from stochastic divergences ... | computer science |
13,213 | A New Algorithm of Speckle Filtering using Stochastic Distances | cs.IT | This paper presents a new approach for filter design based on stochastic
distances and tests between distributions. A window is defined around each
pixel, overlapping samples are compared and only those which pass a
goodness-of-fit test are used to compute the filtered value. The technique is
applied to intensity SAR d... | computer science |
13,214 | Online Robust Subspace Tracking from Partial Information | cs.IT | This paper presents GRASTA (Grassmannian Robust Adaptive Subspace Tracking
Algorithm), an efficient and robust online algorithm for tracking subspaces
from highly incomplete information. The algorithm uses a robust $l^1$-norm cost
function in order to estimate and track non-stationary subspaces when the
streaming data ... | computer science |
13,215 | Speckle Reduction using Stochastic Distances | cs.IT | This paper presents a new approach for filter design based on stochastic
distances and tests between distributions. A window is defined around each
pixel, samples are compared and only those which pass a goodness-of-fit test
are used to compute the filtered value. The technique is applied to intensity
Synthetic Apertur... | computer science |
13,216 | Polarimetric SAR Image Smoothing with Stochastic Distances | cs.IT | Polarimetric Synthetic Aperture Radar (PolSAR) images are establishing as an
important source of information in remote sensing applications. The most
complete format this type of imaging produces consists of complex-valued
Hermitian matrices in every image coordinate and, as such, their visualization
is challenging. Th... | computer science |
13,217 | The Algebraic Approach to Phase Retrieval and Explicit Inversion at the
Identifiability Threshold | math.FA | We study phase retrieval from magnitude measurements of an unknown signal as
an algebraic estimation problem. Indeed, phase retrieval from rank-one and more
general linear measurements can be treated in an algebraic way. It is verified
that a certain number of generic rank-one or generic linear measurements are
suffici... | computer science |
13,218 | Robust Compressed Sensing Under Matrix Uncertainties | cs.IT | Compressed sensing (CS) shows that a signal having a sparse or compressible
representation can be recovered from a small set of linear measurements. In
classical CS theory, the sampling matrix and representation matrix are assumed
to be known exactly in advance. However, uncertainties exist due to sampling
distortion, ... | computer science |
13,219 | Classification and Reconstruction of High-Dimensional Signals from
Low-Dimensional Features in the Presence of Side Information | cs.IT | This paper offers a characterization of fundamental limits on the
classification and reconstruction of high-dimensional signals from
low-dimensional features, in the presence of side information. We consider a
scenario where a decoder has access both to linear features of the signal of
interest and to linear features o... | computer science |
13,220 | Message-passing algorithms for synchronization problems over compact
groups | cs.IT | Various alignment problems arising in cryo-electron microscopy, community
detection, time synchronization, computer vision, and other fields fall into a
common framework of synchronization problems over compact groups such as Z/L,
U(1), or SO(3). The goal of such problems is to estimate an unknown vector of
group eleme... | computer science |
13,221 | Efficient Estimation of Compressible State-Space Models with Application
to Calcium Signal Deconvolution | stat.ML | In this paper, we consider linear state-space models with compressible
innovations and convergent transition matrices in order to model
spatiotemporally sparse transient events. We perform parameter and state
estimation using a dynamic compressed sensing framework and develop an
efficient solution consisting of two nes... | computer science |
13,222 | Collective Intelligence for Control of Distributed Dynamical Systems | cs.LG | We consider the El Farol bar problem, also known as the minority game (W. B.
Arthur, ``The American Economic Review'', 84(2): 406--411 (1994), D. Challet
and Y.C. Zhang, ``Physica A'', 256:514 (1998)). We view it as an instance of
the general problem of how to configure the nodal elements of a distributed
dynamical sys... | computer science |
13,223 | Sharpening Occam's Razor | cs.LG | We provide a new representation-independent formulation of Occam's razor
theorem, based on Kolmogorov complexity. This new formulation allows us to:
(i) Obtain better sample complexity than both length-based and VC-based
versions of Occam's razor theorem, in many applications.
(ii) Achieve a sharper reverse of Occa... | computer science |
13,224 | Sequence Prediction based on Monotone Complexity | cs.AI | This paper studies sequence prediction based on the monotone Kolmogorov
complexity Km=-log m, i.e. based on universal deterministic/one-part MDL. m is
extremely close to Solomonoff's prior M, the latter being an excellent
predictor in deterministic as well as probabilistic environments, where
performance is measured in... | computer science |
13,225 | Distribution of Mutual Information from Complete and Incomplete Data | cs.LG | Mutual information is widely used, in a descriptive way, to measure the
stochastic dependence of categorical random variables. In order to address
questions such as the reliability of the descriptive value, one must consider
sample-to-population inferential approaches. This paper deals with the
posterior distribution o... | computer science |
13,226 | Universal Convergence of Semimeasures on Individual Random Sequences | cs.LG | Solomonoff's central result on induction is that the posterior of a universal
semimeasure M converges rapidly and with probability 1 to the true sequence
generating posterior mu, if the latter is computable. Hence, M is eligible as a
universal sequence predictor in case of unknown mu. Despite some nearby results
and pr... | computer science |
13,227 | Strong Asymptotic Assertions for Discrete MDL in Regression and
Classification | math.ST | We study the properties of the MDL (or maximum penalized complexity)
estimator for Regression and Classification, where the underlying model class
is countable. We show in particular a finite bound on the Hellinger losses
under the only assumption that there is a "true" model contained in the class.
This implies almost... | computer science |
13,228 | Belief Propagation and Beyond for Particle Tracking | cs.IT | We describe a novel approach to statistical learning from particles tracked
while moving in a random environment. The problem consists in inferring
properties of the environment from recorded snapshots. We consider here the
case of a fluid seeded with identical passive particles that diffuse and are
advected by a flow.... | computer science |
13,229 | On Finding Predictors for Arbitrary Families of Processes | cs.LG | The problem is sequence prediction in the following setting. A sequence
$x_1,...,x_n,...$ of discrete-valued observations is generated according to
some unknown probabilistic law (measure) $\mu$. After observing each outcome,
it is required to give the conditional probabilities of the next observation.
The measure $\mu... | computer science |
13,230 | Learning from Untrusted Data | cs.LG | The vast majority of theoretical results in machine learning and statistics
assume that the available training data is a reasonably reliable reflection of
the phenomena to be learned or estimated. Similarly, the majority of machine
learning and statistical techniques used in practice are brittle to the
presence of larg... | computer science |
13,231 | Phase Transitions, Optimal Errors and Optimality of Message-Passing in
Generalized Linear Models | cs.IT | We consider generalized linear models (GLMs) where an unknown $n$-dimensional
signal vector is observed through the application of a random matrix and a
non-linear (possibly probabilistic) componentwise output function. We consider
the models in the high-dimensional limit, where the observation consists of m
points, an... | computer science |
13,232 | Context-Aware Generative Adversarial Privacy | cs.LG | Preserving the utility of published datasets while simultaneously providing
provable privacy guarantees is a well-known challenge. On the one hand,
context-free privacy solutions, such as differential privacy, provide strong
privacy guarantees, but often lead to a significant reduction in utility. On
the other hand, co... | computer science |
13,233 | Aspects of Evolutionary Design by Computers | cs.NE | This paper examines the four main types of Evolutionary Design by computers:
Evolutionary Design Optimisation, Evolutionary Art, Evolutionary Artificial
Life Forms and Creative Evolutionary Design. Definitions for all four areas are
provided. A review of current work in each of these areas is given, with
examples of th... | computer science |
13,234 | Training Reinforcement Neurocontrollers Using the Polytope Algorithm | cs.NE | A new training algorithm is presented for delayed reinforcement learning
problems that does not assume the existence of a critic model and employs the
polytope optimization algorithm to adjust the weights of the action network so
that a simple direct measure of the training performance is maximized.
Experimental result... | computer science |
13,235 | An Efficient Mean Field Approach to the Set Covering Problem | cs.NE | A mean field feedback artificial neural network algorithm is developed and
explored for the set covering problem. A convenient encoding of the inequality
constraints is achieved by means of a multilinear penalty function. An
approximate energy minimum is obtained by iterating a set of mean field
equations, in combinati... | computer science |
13,236 | Alife Model of Evolutionary Emergence of Purposeful Adaptive Behavior | cs.NE | The process of evolutionary emergence of purposeful adaptive behavior is
investigated by means of computer simulations. The model proposed implies that
there is an evolving population of simple agents, which have two natural needs:
energy and reproduction. Any need is characterized quantitatively by a
corresponding mot... | computer science |
13,237 | Design of statistical quality control procedures using genetic
algorithms | cs.NE | In general, we can not use algebraic or enumerative methods to optimize a
quality control (QC) procedure so as to detect the critical random and
systematic analytical errors with stated probabilities, while the probability
for false rejection is minimum. Genetic algorithms (GAs) offer an alternative,
as they do not req... | computer science |
13,238 | Selection of future events from a time series in relation to estimations
of forecasting uncertainty | cs.NE | A new general procedure for a priori selection of more predictable events
from a time series of observed variable is proposed. The procedure is
applicable to time series which contains different types of events that feature
significantly different predictability, or, in other words, to heteroskedastic
time series. A pr... | computer science |
13,239 | Thinking, Learning, and Autonomous Problem Solving | cs.NE | Ever increasing computational power will require methods for automatic
programming. We present an alternative to genetic programming, based on a
general model of thinking and learning. The advantage is that evolution takes
place in the space of constructs and can thus exploit the mathematical
structures of this space. ... | computer science |
13,240 | Optimizing GoTools' Search Heuristics using Genetic Algorithms | cs.NE | GoTools is a program which solves life & death problems in the game of Go.
This paper describes experiments using a Genetic Algorithm to optimize
heuristic weights used by GoTools' tree-search. The complete set of heuristic
weights is composed of different subgroups, each of which can be optimized with
a suitable fitne... | computer science |
13,241 | Predicting Response-Function Results of Electrical/Mechanical Systems
Through Artificial Neural Network | cs.NE | In the present paper a newer application of Artificial Neural Network (ANN)
has been developed i.e., predicting response-function results of
electrical-mechanical system through ANN. This method is specially useful to
complex systems for which it is not possible to find the response-function
because of complexity of th... | computer science |
13,242 | A novel evolutionary formulation of the maximum independent set problem | cs.NE | We introduce a novel evolutionary formulation of the problem of finding a
maximum independent set of a graph. The new formulation is based on the
relationship that exists between a graph's independence number and its acyclic
orientations. It views such orientations as individuals and evolves them with
the aid of evolut... | computer science |
13,243 | Two novel evolutionary formulations of the graph coloring problem | cs.NE | We introduce two novel evolutionary formulations of the problem of coloring
the nodes of a graph. The first formulation is based on the relationship that
exists between a graph's chromatic number and its acyclic orientations. It
views such orientations as individuals and evolves them with the aid of
evolutionary operat... | computer science |
13,244 | On Interference of Signals and Generalization in Feedforward Neural
Networks | cs.NE | This paper studies how the generalization ability of neurons can be affected
by mutual processing of different signals. This study is done on the basis of a
feedforward artificial neural network. The mutual processing of signals can
possibly be a good model of patterns in a set generalized by a neural network
and in ef... | computer science |
13,245 | Feedforward Neural Networks with Diffused Nonlinear Weight Functions | cs.NE | In this paper, feedforward neural networks are presented that have nonlinear
weight functions based on look--up tables, that are specially smoothed in a
regularization called the diffusion. The idea of such a type of networks is
based on the hypothesis that the greater number of adaptive parameters per a
weight functio... | computer science |
13,246 | Mapping weblog communities | cs.NE | Websites of a particular class form increasingly complex networks, and new
tools are needed to map and understand them. A way of visualizing this complex
network is by mapping it. A map highlights which members of the community have
similar interests, and reveals the underlying social network. In this paper, we
will ma... | computer science |
13,247 | Parameter-less Optimization with the Extended Compact Genetic Algorithm
and Iterated Local Search | cs.NE | This paper presents a parameter-less optimization framework that uses the
extended compact genetic algorithm (ECGA) and iterated local search (ILS), but
is not restricted to these algorithms. The presented optimization algorithm
(ILS+ECGA) comes as an extension of the parameter-less genetic algorithm (GA),
where the pa... | computer science |
13,248 | An architecture for massive parallelization of the compact genetic
algorithm | cs.NE | This paper presents an architecture which is suitable for a massive
parallelization of the compact genetic algorithm. The resulting scheme has
three major advantages. First, it has low synchronization costs. Second, it is
fault tolerant, and third, it is scalable.
The paper argues that the benefits that can be obtain... | computer science |
13,249 | A philosophical essay on life and its connections with genetic
algorithms | cs.NE | This paper makes a number of connections between life and various facets of
genetic and evolutionary algorithms research. Specifically, it addresses the
topics of adaptation, multiobjective optimization, decision making, deception,
and search operators, among others. It argues that human life, from birth to
death, is a... | computer science |
13,250 | Genetic Algorithms and Quantum Computation | cs.NE | Recently, researchers have applied genetic algorithms (GAs) to address some
problems in quantum computation. Also, there has been some works in the
designing of genetic algorithms based on quantum theoretical concepts and
techniques. The so called Quantum Evolutionary Programming has two major
sub-areas: Quantum Inspir... | computer science |
13,251 | Efficiency Enhancement of Probabilistic Model Building Genetic
Algorithms | cs.NE | This paper presents two different efficiency-enhancement techniques for
probabilistic model building genetic algorithms. The first technique proposes
the use of a mutation operator which performs local search in the sub-solution
neighborhood identified through the probabilistic model. The second technique
proposes buil... | computer science |
13,252 | Let's Get Ready to Rumble: Crossover Versus Mutation Head to Head | cs.NE | This paper analyzes the relative advantages between crossover and mutation on
a class of deterministic and stochastic additively separable problems. This
study assumes that the recombination and mutation operators have the knowledge
of the building blocks (BBs) and effectively exchange or search among competing
BBs. Fa... | computer science |
13,253 | Designing Competent Mutation Operators via Probabilistic Model Building
of Neighborhoods | cs.NE | This paper presents a competent selectomutative genetic algorithm (GA), that
adapts linkage and solves hard problems quickly, reliably, and accurately. A
probabilistic model building process is used to automatically identify key
building blocks (BBs) of the search problem. The mutation operator uses the
probabilistic m... | computer science |
13,254 | Efficiency Enhancement of Genetic Algorithms via Building-Block-Wise
Fitness Estimation | cs.NE | This paper studies fitness inheritance as an efficiency enhancement technique
for a class of competent genetic algorithms called estimation distribution
algorithms. Probabilistic models of important sub-solutions are developed to
estimate the fitness of a proportion of individuals in the population, thereby
avoiding co... | computer science |
13,255 | Portfolio selection using neural networks | cs.NE | In this paper we apply a heuristic method based on artificial neural networks
in order to trace out the efficient frontier associated to the portfolio
selection problem. We consider a generalization of the standard Markowitz
mean-variance model which includes cardinality and bounding constraints. These
constraints ensu... | computer science |
13,256 | Obtaining Membership Functions from a Neuron Fuzzy System extended by
Kohonen Network | cs.NE | This article presents the Neo-Fuzzy-Neuron Modified by Kohonen Network
(NFN-MK), an hybrid computational model that combines fuzzy system technique
and artificial neural networks. Its main task consists in the automatic
generation of membership functions, in particular, triangle forms, aiming a
dynamic modeling of a sy... | computer science |
13,257 | A Neural-Network Technique for Recognition of Filaments in Solar Images | cs.NE | We describe a new neural-network technique developed for an automated
recognition of solar filaments visible in the hydrogen H-alpha line full disk
spectroheliograms. This technique allows neural networks learn from a few image
fragments labelled manually to recognize the single filaments depicted on a
local background... | computer science |
13,258 | A New Kind of Hopfield Networks for Finding Global Optimum | cs.NE | The Hopfield network has been applied to solve optimization problems over
decades. However, it still has many limitations in accomplishing this task.
Most of them are inherited from the optimization algorithms it implements. The
computation of a Hopfield network, defined by a set of difference equations,
can easily be ... | computer science |
13,259 | Visual Character Recognition using Artificial Neural Networks | cs.NE | The recognition of optical characters is known to be one of the earliest
applications of Artificial Neural Networks, which partially emulate human
thinking in the domain of artificial intelligence. In this paper, a simplified
neural approach to recognition of optical or visual characters is portrayed and
discussed. The... | computer science |
13,260 | Artificial Neural Networks and their Applications | cs.NE | The Artificial Neural network is a functional imitation of simplified model
of the biological neurons and their goal is to construct useful computers for
real world problems. The ANN applications have increased dramatically in the
last few years fired by both theoretical and practical applications in a wide
variety of ... | computer science |
13,261 | Distant generalization by feedforward neural networks | cs.NE | This paper discusses the notion of generalization of training samples over
long distances in the input space of a feedforward neural network. Such a
generalization might occur in various ways, that differ in how great the
contribution of different training features should be.
The structure of a neuron in a feedforwar... | computer science |
13,262 | A dissipative particle swarm optimization | cs.NE | A dissipative particle swarm optimization is developed according to the
self-organization of dissipative structure. The negative entropy is introduced
to construct an opening dissipative system that is far-from-equilibrium so as
to driving the irreversible evolution process with better fitness. The testing
of two multi... | computer science |
13,263 | Optimizing semiconductor devices by self-organizing particle swarm | cs.NE | A self-organizing particle swarm is presented. It works in dissipative state
by employing the small inertia weight, according to experimental analysis on a
simplified model, which with fast convergence. Then by recognizing and
replacing inactive particles according to the process deviation information of
device paramet... | computer science |
13,264 | Handling equality constraints by adaptive relaxing rule for swarm
algorithms | cs.NE | The adaptive constraints relaxing rule for swarm algorithms to handle with
the problems with equality constraints is presented. The feasible space of such
problems may be similiar to ridge function class, which is hard for applying
swarm algorithms. To enter the solution space more easily, the relaxed quasi
feasible sp... | computer science |
13,265 | Handling boundary constraints for numerical optimization by particle
swarm flying in periodic search space | cs.NE | The periodic mode is analyzed together with two conventional boundary
handling modes for particle swarm. By providing an infinite space that
comprises periodic copies of original search space, it avoids possible
disorganizing of particle swarm that is induced by the undesired mutations at
the boundary. The results on b... | computer science |
13,266 | SWAF: Swarm Algorithm Framework for Numerical Optimization | cs.NE | A swarm algorithm framework (SWAF), realized by agent-based modeling, is
presented to solve numerical optimization problems. Each agent is a bare bones
cognitive architecture, which learns knowledge by appropriately deploying a set
of simple rules in fast and frugal heuristics. Two essential categories of
rules, the ge... | computer science |
13,267 | Framework for Hopfield Network based Adaptive routing - A design level
approach for adaptive routing phenomena with Artificial Neural Network | cs.NE | Routing, as a basic phenomena, by itself, has got umpteen scopes to analyse,
discuss and arrive at an optimal solution for the technocrats over years.
Routing is analysed based on many factors; few key constraints that decide the
factors are communication medium, time dependency, information source nature.
Parametric r... | computer science |
13,268 | Does a Plane Imitate a Bird? Does Computer Vision Have to Follow
Biological Paradigms? | cs.NE | We posit a new paradigm for image information processing. For the last 25
years, this task was usually approached in the frame of Treisman's two-stage
paradigm [1]. The latter supposes an unsupervised, bottom-up directed process
of preliminary information pieces gathering at the lower processing stages and
a supervised... | computer science |
13,269 | Discrete Network Dynamics. Part 1: Operator Theory | cs.NE | An operator algebra implementation of Markov chain Monte Carlo algorithms for
simulating Markov random fields is proposed. It allows the dynamics of networks
whose nodes have discrete state spaces to be specified by the action of an
update operator that is composed of creation and annihilation operators. This
formulati... | computer science |
13,270 | Evolino for recurrent support vector machines | cs.NE | Traditional Support Vector Machines (SVMs) need pre-wired finite time windows
to predict and classify time series. They do not have an internal state
necessary to deal with sequences involving arbitrary long-term dependencies.
Here we introduce a new class of recurrent, truly sequential SVM-like devices
with internal a... | computer science |
13,271 | Réseaux d'Automates de Caianiello Revisité | cs.NE | We exhibit a family of neural networks of McCulloch and Pitts of size $2nk+2$
which can be simulated by a neural networks of Caianiello of size $2n+2$ and
memory length $k$. This simulation allows us to find again one of the result of
the following article: [Cycles exponentiels des r\'{e}seaux de Caianiello et
compteur... | computer science |
13,272 | On the utility of the multimodal problem generator for assessing the
performance of Evolutionary Algorithms | cs.NE | This paper looks in detail at how an evolutionary algorithm attempts to solve
instances from the multimodal problem generator. The paper shows that in order
to consistently reach the global optimum, an evolutionary algorithm requires a
population size that should grow at least linearly with the number of peaks. It
is a... | computer science |
13,273 | Revisiting Evolutionary Algorithms with On-the-Fly Population Size
Adjustment | cs.NE | In an evolutionary algorithm, the population has a very important role as its
size has direct implications regarding solution quality, speed, and
reliability. Theoretical studies have been done in the past to investigate the
role of population sizing in evolutionary algorithms. In addition to those
studies, several sel... | computer science |
13,274 | Lamarckian Evolution and the Baldwin Effect in Evolutionary Neural
Networks | cs.NE | Hybrid neuro-evolutionary algorithms may be inspired on Darwinian or
Lamarckian evolu- tion. In the case of Darwinian evolution, the Baldwin effect,
that is, the progressive incorporation of learned characteristics to the
genotypes, can be observed and leveraged to improve the search. The purpose of
this paper is to ca... | computer science |
13,275 | The Basic Kak Neural Network with Complex Inputs | cs.NE | The Kak family of neural networks is able to learn patterns quickly, and this
speed of learning can be a decisive advantage over other competing models in
many applications. Amongst the implementations of these networks are those
using reconfigurable networks, FPGAs and optical networks. In some
applications, it is use... | computer science |
13,276 | The NoN Approach to Autonomic Face Recognition | cs.NE | A method of autonomic face recognition based on the biologically plausible
network of networks (NoN) model of information processing is presented. The NoN
model is based on locally parallel and globally coordinated transformations in
which the neurons or computational units form distributed networks, which
themselves l... | computer science |
13,277 | Theoretical Properties of Projection Based Multilayer Perceptrons with
Functional Inputs | cs.NE | Many real world data are sampled functions. As shown by Functional Data
Analysis (FDA) methods, spectra, time series, images, gesture recognition data,
etc. can be processed more efficiently if their functional nature is taken into
account during the data analysis process. This is done by extending standard
data analys... | computer science |
13,278 | Modelling the Probability Density of Markov Sources | cs.NE | This paper introduces an objective function that seeks to minimise the
average total number of bits required to encode the joint state of all of the
layers of a Markov source. This type of encoder may be applied to the problem
of optimising the bottom-up (recognition model) and top-down (generative model)
connections i... | computer science |
13,279 | Neural Networks with Complex and Quaternion Inputs | cs.NE | This article investigates Kak neural networks, which can be instantaneously
trained, for complex and quaternion inputs. The performance of the basic
algorithm has been analyzed and shown how it provides a plausible model of
human perception and understanding of images. The motivation for studying
quaternion inputs is t... | computer science |
13,280 | Problem Evolution: A new approach to problem solving systems | cs.NE | In this paper we present a novel tool to evaluate problem solving systems.
Instead of using a system to solve a problem, we suggest using the problem to
evaluate the system. By finding a numerical representation of a problem's
complexity, one can implement genetic algorithm to search for the most complex
problem the gi... | computer science |
13,281 | V-like formations in flocks of artificial birds | cs.NE | We consider flocks of artificial birds and study the emergence of V-like
formations during flight. We introduce a small set of fully distributed
positioning rules to guide the birds' movements and demonstrate, by means of
simulations, that they tend to lead to stabilization into several of the
well-known V-like formati... | computer science |
13,282 | On the possibility of making the complete computer model of a human
brain | cs.NE | The development of the algorithm of a neural network building by the
corresponding parts of a DNA code is discussed. | computer science |
13,283 | Risk Assessment Algorithms Based On Recursive Neural Networks | cs.NE | The assessment of highly-risky situations at road intersections have been
recently revealed as an important research topic within the context of the
automotive industry. In this paper we shall introduce a novel approach to
compute risk functions by using a combination of a highly non-linear processing
model in conjunct... | computer science |
13,284 | Actin - Technical Report | cs.NE | The Boolean satisfiability problem (SAT) can be solved efficiently with
variants of the DPLL algorithm. For industrial SAT problems, DPLL with conflict
analysis dependent dynamic decision heuristics has proved to be particularly
efficient, e.g. in Chaff. In this work, algorithms that initialize the variable
activity va... | computer science |
13,285 | Improved Neural Modeling of Real-World Systems Using Genetic Algorithm
Based Variable Selection | cs.NE | Neural network models of real-world systems, such as industrial processes,
made from sensor data must often rely on incomplete data. System states may not
all be known, sensor data may be biased or noisy, and it is not often known
which sensor data may be useful for predictive modelling. Genetic algorithms
may be used ... | computer science |
13,286 | From Royal Road to Epistatic Road for Variable Length Evolution
Algorithm | cs.NE | Although there are some real world applications where the use of variable
length representation (VLR) in Evolutionary Algorithm is natural and suitable,
an academic framework is lacking for such representations. In this work we
propose a family of tunable fitness landscapes based on VLR of genotypes. The
fitness landsc... | computer science |
13,287 | Where are Bottlenecks in NK Fitness Landscapes? | cs.NE | Usually the offspring-parent fitness correlation is used to visualize and
analyze some caracteristics of fitness landscapes such as evolvability. In this
paper, we introduce a more general representation of this correlation, the
Fitness Cloud (FC). We use the bottleneck metaphor to emphasise fitness levels
in landscape... | computer science |
13,288 | Scuba Search : when selection meets innovation | cs.NE | We proposed a new search heuristic using the scuba diving metaphor. This
approach is based on the concept of evolvability and tends to exploit
neutrality in fitness landscape. Despite the fact that natural evolution does
not directly select for evolvability, the basic idea behind the scuba search
heuristic is to explic... | computer science |
13,289 | How to use the Scuba Diving metaphor to solve problem with neutrality ? | cs.NE | We proposed a new search heuristic using the scuba diving metaphor. This
approach is based on the concept of evolvability and tends to exploit
neutrality which exists in many real-world problems. Despite the fact that
natural evolution does not directly select for evolvability, the basic idea
behind the scuba search he... | computer science |
13,290 | The universal evolutionary computer based on super-recursive algorithms
of evolvability | cs.NE | This work exposes which mechanisms and procesess in the Nature of evolution
compute a function not computable by Turing machine. The computer with
intelligence that is not higher than one bacteria population could have, but
with efficency to solve the problems that are non-computable by Turing machine
is represented. T... | computer science |
13,291 | Representation of Functional Data in Neural Networks | cs.NE | Functional Data Analysis (FDA) is an extension of traditional data analysis
to functional data, for example spectra, temporal series, spatio-temporal
images, gesture recognition data, etc. Functional data are rarely known in
practice; usually a regular or irregular sampling is known. For this reason,
some processing is... | computer science |
13,292 | Functional Multi-Layer Perceptron: a Nonlinear Tool for Functional Data
Analysis | cs.NE | In this paper, we study a natural extension of Multi-Layer Perceptrons (MLP)
to functional inputs. We show that fundamental results for classical MLP can be
extended to functional MLP. We obtain universal approximation results that show
the expressive power of functional MLP is comparable to that of numerical MLP.
We o... | computer science |
13,293 | GUIDE: Unifying Evolutionary Engines through a Graphical User Interface | cs.NE | Many kinds of Evolutionary Algorithms (EAs) have been described in the
literature since the last 30 years. However, though most of them share a common
structure, no existing software package allows the user to actually shift from
one model to another by simply changing a few parameters, e.g. in a single
window of a Gra... | computer science |
13,294 | Digital Ecosystems: Optimisation by a Distributed Intelligence | cs.NE | Can intelligence optimise Digital Ecosystems? How could a distributed
intelligence interact with the ecosystem dynamics? Can the software components
that are part of genetic selection be intelligent in themselves, as in an
adaptive technology? We consider the effect of a distributed intelligence
mechanism on the evolut... | computer science |
13,295 | Digital Ecosystems: Stability of Evolving Agent Populations | cs.NE | Stability is perhaps one of the most desirable features of any engineered
system, given the importance of being able to predict its response to various
environmental conditions prior to actual deployment. Engineered systems are
becoming ever more complex, approaching the same levels of biological
ecosystems, and so the... | computer science |
13,296 | Digital Ecosystems: Evolving Service-Oriented Architectures | cs.NE | We view Digital Ecosystems to be the digital counterparts of biological
ecosystems, exploiting the self-organising properties of biological ecosystems,
which are considered to be robust, self-organising and scalable architectures
that can automatically solve complex, dynamic problems. Digital Ecosystems are
a novel opt... | computer science |
13,297 | Creating a Digital Ecosystem: Service-Oriented Architectures with
Distributed Evolutionary Computing | cs.NE | We start with a discussion of the relevant literature, including Nature
Inspired Computing as a framework in which to understand this work, and the
process of biomimicry to be used in mimicking the necessary biological
processes to create Digital Ecosystems. We then consider the relevant
theoretical ecology in creating... | computer science |
13,298 | Multi-Layer Perceptrons and Symbolic Data | cs.NE | In some real world situations, linear models are not sufficient to represent
accurately complex relations between input variables and output variables of a
studied system. Multilayer Perceptrons are one of the most successful
non-linear regression tool but they are unfortunately restricted to inputs and
outputs that be... | computer science |
13,299 | Accélération des cartes auto-organisatrices sur tableau de
dissimilarités par séparation et évaluation | cs.NE | In this paper, a new implementation of the adaptation of Kohonen
self-organising maps (SOM) to dissimilarity matrices is proposed. This
implementation relies on the branch and bound principle to reduce the algorithm
running time. An important property of this new approach is that the obtained
algorithm produces exactly... | computer science |
13,300 | A data-driven functional projection approach for the selection of
feature ranges in spectra with ICA or cluster analysis | cs.NE | Prediction problems from spectra are largely encountered in chemometry. In
addition to accurate predictions, it is often needed to extract information
about which wavelengths in the spectra contribute in an effective way to the
quality of the prediction. This implies to select wavelengths (or wavelength
intervals), a p... | computer science |
13,301 | Using Bayesian Blocks to Partition Self-Organizing Maps | cs.NE | Self organizing maps (SOMs) are widely-used for unsupervised classification.
For this application, they must be combined with some partitioning scheme that
can identify boundaries between distinct regions in the maps they produce. We
discuss a novel partitioning scheme for SOMs based on the Bayesian Blocks
segmentation... | computer science |
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