arXiv
(422,153 recursos)
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Mostrando recursos 161 - 180 de 9,277
161.
Sufficient conditions for convergence of Loopy Belief Propagation - Mooij, J. M.; Kappen, H. J.
We derive novel conditions that guarantee convergence of Loopy Belief
Propagation (also known as the Sum-Product algorithm) to a unique fixed point.
Our results are provably stronger than existing sufficient conditions. We show
that the improvement can be quite substantial; in particular, for binary
variables with (anti-)ferromagnetic interactions, our conditions seem to be
sharp.
162.
Convexity Analysis of Snake Models Based on Hamiltonian Formulation - Giraldi, Gilson Antonio; de Oliveira, Antonio Alberto Fernandes
This paper presents a convexity analysis for the dynamic snake model based on
the Potential Energy functional and the Hamiltonian formulation of the
classical mechanics. First we see the snake model as a dynamical system whose
singular points are the borders we seek. Next we show that a necessary
condition for a singular point to be an attractor is that the energy functional
is strictly convex in a neighborhood of it, that means, if the singular point
is a local minimum of the potential energy. As a consequence of this analysis,
a local expression relating the dynamic parameters and the rate of convergence
arises. Such results link the...
163.
Critical Point for Maximum Likelihood Decoding of Linear Block Codes - Fossorier, Marc
In this letter, the SNR value at which the error performance curve of a soft
decision maximum likelihood decoder reaches the slope corresponding to the code
minimum distance is determined for a random code. Based on this value, referred
to as the critical point, new insight about soft bounded distance decoding of
random-like codes (and particularly Reed-Solomon codes) is provided.
164.
DTN Routing in a Mobility Pattern Space - Leguay, Jeremie; Friedman, Timur; Conan, Vania
Routing in Delay Tolerant Networks (DTNs) benefits considerably if one can
take advantage of knowledge concerning node mobility. The main contribution of
this paper is the definition of a generic routing scheme for DTNs using a
high-dimensional Euclidean space constructed upon nodes' mobility patterns. For
example, nodes are represented as points having as coordinates their
probability of being found in each possible location. We present simulation
results indicating that such a scheme can be beneficial in a scenario inspired
by studies done on real mobility traces. This work should open the way to
further use of the virtual space formalism in DTN routing.
165.
Analyzing Worms and Network Traffic using Compression - Wehner, Stephanie
Internet worms have become a widespread threat to system and network
operations. In order to fight them more efficiently, it is necessary to analyze
newly discovered worms and attack patterns. This paper shows how techniques
based on Kolmogorov Complexity can help in the analysis of internet worms and
network traffic. Using compression, different species of worms can be clustered
by type. This allows us to determine whether an unknown worm binary could in
fact be a later version of an existing worm in an extremely simple, automated,
manner. This may become a useful tool in the initial analysis of malicious
binaries. Furthermore, compression can also be useful to...
166.
Pushdown dimension - Doty, David; Nichols, Jared
This paper develops the theory of pushdown dimension and explores its
relationship with finite-state dimension. Pushdown dimension is trivially
bounded above by finite-state dimension for all sequences, since a pushdown
gambler can simulate any finite-state gambler. We show that for every rational
0 < d < 1, there exists a sequence with finite-state dimension d whose pushdown
dimension is at most d/2. This establishes a quantitative analogue of the
well-known fact that pushdown automata decide strictly more languages than
finite automata.
167.
Bounds on the Entropy of Patterns of I.I.D. Sequences - Shamir, Gil I.
Bounds on the entropy of patterns of sequences generated by independently
identically distributed (i.i.d.) sources are derived. A pattern is a sequence
of indices that contains all consecutive integer indices in increasing order of
first occurrence. If the alphabet of a source that generated a sequence is
unknown, the inevitable cost of coding the unknown alphabet symbols can be
exploited to create the pattern of the sequence. This pattern can in turn be
compressed by itself. The bounds derived here are functions of the i.i.d.
source entropy, alphabet size, and letter probabilities. It is shown that for
large alphabets, the pattern entropy must decrease from the i.i.d. one....
168.
Learning Multi-Class Neural-Network Models from Electroencephalograms - Schetinin, Vitaly; Schult, Joachim; Scheidt, Burkhart; Kuriakin, Valery
We describe a new algorithm for learning multi-class neural-network models
from large-scale clinical electroencephalograms (EEGs). This algorithm trains
hidden neurons separately to classify all the pairs of classes. To find best
pairwise classifiers, our algorithm searches for input variables which are
relevant to the classification problem. Despite patient variability and heavily
overlapping classes, a 16-class model learnt from EEGs of 65 sleeping newborns
correctly classified 80.8% of the training and 80.1% of the testing examples.
Additionally, the neural-network model provides a probabilistic interpretation
of decisions.
169.
A Neural-Network Technique for Recognition of Filaments in Solar Images - Zharkova, V. V.; Schetinin, V.
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. The trained network is able to recognize filaments depicted
on the backgrounds with variations in brightness caused by atmospherics
distortions. Despite the difference in backgrounds in our experiments the
neural network has properly recognized filaments in the testing image
fragments. Using a parabolic activation function we extend this technique to
recognize multiple solar filaments which may appear in one fragment.
170.
Learning from Web: Review of Approaches - Schetinin, Vitaly
Knowledge discovery is defined as non-trivial extraction of implicit,
previously unknown and potentially useful information from given data.
Knowledge extraction from web documents deals with unstructured, free-format
documents whose number is enormous and rapidly growing. The artificial neural
networks are well suitable to solve a problem of knowledge discovery from web
documents because trained networks are able more accurately and easily to
classify the learning and testing examples those represent the text mining
domain. However, the neural networks that consist of large number of weighted
connections and activation units often generate the incomprehensible and
hard-to-understand models of text classification. This problem may be also
addressed to most powerful recurrent neural...
171.
A Learning Algorithm for Evolving Cascade Neural Networks - Schetinin, Vitaly
A new learning algorithm for Evolving Cascade Neural Networks (ECNNs) is
described. An ECNN starts to learn with one input node and then adding new
inputs as well as new hidden neurons evolves it. The trained ECNN has a nearly
minimal number of input and hidden neurons as well as connections. The
algorithm was successfully applied to classify artifacts and normal segments in
clinical electroencephalograms (EEGs). The EEG segments were visually labeled
by EEG-viewer. The trained ECNN has correctly classified 96.69% of the testing
segments. It is slightly better than a standard fully connected neural network.
172.
Self-Organizing Multilayered Neural Networks of Optimal Complexity - Schetinin, V.
The principles of self-organizing the neural networks of optimal complexity
is considered under the unrepresentative learning set. The method of
self-organizing the multi-layered neural networks is offered and used to train
the logical neural networks which were applied to the medical diagnostics.
173.
Diagnostic Rule Extraction Using Neural Networks - Schetinin, Vitaly; Brazhnikov, Anatoly
The neural networks have trained on incomplete sets that a doctor could
collect. Trained neural networks have correctly classified all the presented
instances. The number of intervals entered for encoding the quantitative
variables is equal two. The number of features as well as the number of neurons
and layers in trained neural networks was minimal. Trained neural networks are
adequately represented as a set of logical formulas that more comprehensible
and easy-to-understand. These formulas are as the syndrome-complexes, which may
be easily tabulated and represented as a diagnostic table that the doctors
usually use. Decision rules provide the evaluations of their confidence in
which interested a doctor. Conducted clinical...
174.
Polynomial Neural Networks Learnt to Classify EEG Signals - Schetinin, Vitaly
A neural network based technique is presented, which is able to successfully
extract polynomial classification rules from labeled electroencephalogram (EEG)
signals. To represent the classification rules in an analytical form, we use
the polynomial neural networks trained by a modified Group Method of Data
Handling (GMDH). The classification rules were extracted from clinical EEG data
that were recorded from an Alzheimer patient and the sudden death risk
patients. The third data is EEG recordings that include the normal and artifact
segments. These EEG data were visually identified by medical experts. The
extracted polynomial rules verified on the testing EEG data allow to correctly
classify 72% of the risk group...
175.
A Neural Network Decision Tree for Learning Concepts from EEG Data - Schetinin, Vitaly
To learn the multi-class conceptions from the electroencephalogram (EEG) data
we developed a neural network decision tree (DT), that performs the linear
tests, and a new training algorithm. We found that the known methods fail
inducting the classification models when the data are presented by the features
some of them are irrelevant, and the classes are heavily overlapped. To train
the DT, our algorithm exploits a bottom up search of the features that provide
the best classification accuracy of the linear tests. We applied the developed
algorithm to induce the DT from the large EEG dataset consisted of 65 patients
belonging to 16 age groups. In these recordings...
176.
Universal Minimax Discrete Denoising under Channel Uncertainty - Gemelos, George; Sigurjonsson, Styrmir; Weissman, Tsachy
The goal of a denoising algorithm is to recover a signal from its
noise-corrupted observations. Perfect recovery is seldom possible and
performance is measured under a given single-letter fidelity criterion. For
discrete signals corrupted by a known discrete memoryless channel, the DUDE was
recently shown to perform this task asymptotically optimally, without knowledge
of the statistical properties of the source. In the present work we address the
scenario where, in addition to the lack of knowledge of the source statistics,
there is also uncertainty in the channel characteristics. We propose a family
of discrete denoisers and establish their asymptotic optimality under a minimax
performance criterion which we argue is...
177.
Selection in Scale-Free Small World - Palotai, Zs.; Farkas, Cs.; Lorincz, A.
In this paper we compare the performance characteristics of our selection
based learning algorithm for Web crawlers with the characteristics of the
reinforcement learning algorithm. The task of the crawlers is to find new
information on the Web. The selection algorithm, called weblog update, modifies
the starting URL lists of our crawlers based on the found URLs containing new
information. The reinforcement learning algorithm modifies the URL orderings of
the crawlers based on the received reinforcements for submitted documents. We
performed simulations based on data collected from the Web. The collected
portion of the Web is typical and exhibits scale-free small world (SFSW)
structure. We have found that on...
178.
Self-Organization of the Neuron Collective of Optimal Complexity - Schetinin, V.; Kostunin, A.
The optimal complexity of neural networks is achieved when the
self-organization principles is used to eliminate the contradictions existing
in accordance with the K. Godel theorem about incompleteness of the systems
based on axiomatics. The principle of S. Beer exterior addition the Heuristic
Group Method of Data Handling by A. Ivakhnenko realized is used.
179.
A Neural-Network Technique to Learn Concepts from Electroencephalograms - Schetinin, Vitaly; Schult, Joachim
A new technique is presented developed to learn multi-class concepts from
clinical electroencephalograms. A desired concept is represented as a neuronal
computational model consisting of the input, hidden, and output neurons. In
this model the hidden neurons learn independently to classify the
electroencephalogram segments presented by spectral and statistical features.
This technique has been applied to the electroencephalogram data recorded from
65 sleeping healthy newborns in order to learn a brain maturation concept of
newborns aged between 35 and 51 weeks. The 39399 and 19670 segments from these
data have been used for learning and testing the concept, respectively. As a
result, the concept has correctly classified 80.1% of...
180.
Proceedings of the Pacific Knowledge Acquisition Workshop 2004 - Kang, Byeong Ho; Hoffmann, Achim; Yamaguchi, Takahira; Yeap, Wai Kiang
Artificial intelligence (AI) research has evolved over the last few decades
and knowledge acquisition research is at the core of AI research. PKAW-04 is
one of three international knowledge acquisition workshops held in the
Pacific-Rim, Canada and Europe over the last two decades. PKAW-04 has a strong
emphasis on incremental knowledge acquisition, machine learning, neural nets
and active mining.
The proceedings contain 19 papers that were selected by the program committee
among 24 submitted papers. All papers were peer reviewed by at least two
reviewers. The papers in these proceedings cover the methods and tools as well
as the applications related to develop expert systems or knowledge based
systems.
161.
