896 research outputs found
Optimality of Universal Bayesian Sequence Prediction for General Loss and Alphabet
Various optimality properties of universal sequence predictors based on
Bayes-mixtures in general, and Solomonoff's prediction scheme in particular,
will be studied. The probability of observing at time , given past
observations can be computed with the chain rule if the true
generating distribution of the sequences is known. If
is unknown, but known to belong to a countable or continuous class \M
one can base ones prediction on the Bayes-mixture defined as a
-weighted sum or integral of distributions \nu\in\M. The cumulative
expected loss of the Bayes-optimal universal prediction scheme based on
is shown to be close to the loss of the Bayes-optimal, but infeasible
prediction scheme based on . We show that the bounds are tight and that no
other predictor can lead to significantly smaller bounds. Furthermore, for
various performance measures, we show Pareto-optimality of and give an
Occam's razor argument that the choice for the weights
is optimal, where is the length of the shortest program describing
. The results are applied to games of chance, defined as a sequence of
bets, observations, and rewards. The prediction schemes (and bounds) are
compared to the popular predictors based on expert advice. Extensions to
infinite alphabets, partial, delayed and probabilistic prediction,
classification, and more active systems are briefly discussed.Comment: 34 page
Graph Signal Processing: Overview, Challenges and Applications
Research in Graph Signal Processing (GSP) aims to develop tools for
processing data defined on irregular graph domains. In this paper we first
provide an overview of core ideas in GSP and their connection to conventional
digital signal processing. We then summarize recent developments in developing
basic GSP tools, including methods for sampling, filtering or graph learning.
Next, we review progress in several application areas using GSP, including
processing and analysis of sensor network data, biological data, and
applications to image processing and machine learning. We finish by providing a
brief historical perspective to highlight how concepts recently developed in
GSP build on top of prior research in other areas.Comment: To appear, Proceedings of the IEE
Avtonomna segmentacija slik z Markovim slučajnim poljem
Segmentacija slik je zelo raziskovano področje, za katero so na voljo številni algoritmi. Naš cilj je segmentacija slike s pomočjo superpikslov na več skladnih delov in na nenadzorovan način. Da bi to dosegli, predlagamo iterativni segmentacijski algoritem. Algoritem predstavlja sliko kot slučajno polje Markova (MRF), katerega vozlišča so superpiksli, ki imajo barvne in teksturne atribute. Superpikslom dodelimo oznake na podlagi njihovih atributov s pomočjo metode podpornih vektorjev (SVM) in že omenjenega MRF in iterativno zmanjšujemo število segmentov. Negotovo segmentacijo po vsaki iteraciji se izboljšuje in rezultat je segmentacija slike na več semantično smiselnih delov, brez pomoči uporabnika. Algoritem je bil testiran na segmentacijsko podatkovno bazo in F ocene so podobne najsodobnejšim algoritmom. Glede fragmentacije slike naš pristop bistveno prekosi stanje tehnike z zmanjšanjem števila segmentov, iz katerih je sestavljen predmet zanimanja.Image segmentation is a widely-researched topic with many algorithms available. Our goal is to segment an image, in an unsupervised way, into several coherent parts with the help of superpixels. To achieve that, we propose an iterative segmentation algorithm. The algorithm models the image by a Markov random field, whose nodes are the superpixels, and each node has both color and texture features. The superpixels are assigned labels according to their features with the help of support vector machines and the aforementioned MRF and the number of segments is iteratively reduced. The result is a segmentation of an image into several regions with requiring any user input. The segmentation algorithm was tested on a standard evaluation database, and performs on par with state-of-the-art segmentation algorithms in F-measures. In terms of oversegmentation, our approach significantly outperforms the state of the art by greatly reducing the oversegmentation of the object of interest
- …