1,276 research outputs found
Probabilistic Dynamic Logic of Phenomena and Cognition
The purpose of this paper is to develop further the main concepts of
Phenomena Dynamic Logic (P-DL) and Cognitive Dynamic Logic (C-DL), presented in
the previous paper. The specific character of these logics is in matching
vagueness or fuzziness of similarity measures to the uncertainty of models.
These logics are based on the following fundamental notions: generality
relation, uncertainty relation, simplicity relation, similarity maximization
problem with empirical content and enhancement (learning) operator. We develop
these notions in terms of logic and probability and developed a Probabilistic
Dynamic Logic of Phenomena and Cognition (P-DL-PC) that relates to the scope of
probabilistic models of brain. In our research the effectiveness of suggested
formalization is demonstrated by approximation of the expert model of breast
cancer diagnostic decisions. The P-DL-PC logic was previously successfully
applied to solving many practical tasks and also for modelling of some
cognitive processes.Comment: 6 pages, WCCI 2010 IEEE World Congress on Computational Intelligence
July, 18-23, 2010 - CCIB, Barcelona, Spain, IJCNN, IEEE Catalog Number:
CFP1OUS-DVD, ISBN: 978-1-4244-6917-8, pp. 3361-336
Chord Label Personalization through Deep Learning of Integrated Harmonic Interval-based Representations
The increasing accuracy of automatic chord estimation systems, the
availability of vast amounts of heterogeneous reference annotations, and
insights from annotator subjectivity research make chord label personalization
increasingly important. Nevertheless, automatic chord estimation systems are
historically exclusively trained and evaluated on a single reference
annotation. We introduce a first approach to automatic chord label
personalization by modeling subjectivity through deep learning of a harmonic
interval-based chord label representation. After integrating these
representations from multiple annotators, we can accurately personalize chord
labels for individual annotators from a single model and the annotators' chord
label vocabulary. Furthermore, we show that chord personalization using
multiple reference annotations outperforms using a single reference annotation.Comment: Proceedings of the First International Conference on Deep Learning
and Music, Anchorage, US, May, 2017 (arXiv:1706.08675v1 [cs.NE]
Partitioning Relational Matrices of Similarities or Dissimilarities using the Value of Information
In this paper, we provide an approach to clustering relational matrices whose
entries correspond to either similarities or dissimilarities between objects.
Our approach is based on the value of information, a parameterized,
information-theoretic criterion that measures the change in costs associated
with changes in information. Optimizing the value of information yields a
deterministic annealing style of clustering with many benefits. For instance,
investigators avoid needing to a priori specify the number of clusters, as the
partitions naturally undergo phase changes, during the annealing process,
whereby the number of clusters changes in a data-driven fashion. The
global-best partition can also often be identified.Comment: Submitted to the IEEE International Conference on Acoustics, Speech,
and Signal Processing (ICASSP
A Convolutional Neural Network for the Automatic Diagnosis of Collagen VI related Muscular Dystrophies
The development of machine learning systems for the diagnosis of rare
diseases is challenging mainly due the lack of data to study them. Despite this
challenge, this paper proposes a system for the Computer Aided Diagnosis (CAD)
of low-prevalence, congenital muscular dystrophies from confocal microscopy
images. The proposed CAD system relies on a Convolutional Neural Network (CNN)
which performs an independent classification for non-overlapping patches tiling
the input image, and generates an overall decision summarizing the individual
decisions for the patches on the query image. This decision scheme points to
the possibly problematic areas in the input images and provides a global
quantitative evaluation of the state of the patients, which is fundamental for
diagnosis and to monitor the efficiency of therapies.Comment: Submitted for review to Expert Systems With Application
PSO based Neural Networks vs. Traditional Statistical Models for Seasonal Time Series Forecasting
Seasonality is a distinctive characteristic which is often observed in many
practical time series. Artificial Neural Networks (ANNs) are a class of
promising models for efficiently recognizing and forecasting seasonal patterns.
In this paper, the Particle Swarm Optimization (PSO) approach is used to
enhance the forecasting strengths of feedforward ANN (FANN) as well as Elman
ANN (EANN) models for seasonal data. Three widely popular versions of the basic
PSO algorithm, viz. Trelea-I, Trelea-II and Clerc-Type1 are considered here.
The empirical analysis is conducted on three real-world seasonal time series.
Results clearly show that each version of the PSO algorithm achieves notably
better forecasting accuracies than the standard Backpropagation (BP) training
method for both FANN and EANN models. The neural network forecasting results
are also compared with those from the three traditional statistical models,
viz. Seasonal Autoregressive Integrated Moving Average (SARIMA), Holt-Winters
(HW) and Support Vector Machine (SVM). The comparison demonstrates that both
PSO and BP based neural networks outperform SARIMA, HW and SVM models for all
three time series datasets. The forecasting performances of ANNs are further
improved through combining the outputs from the three PSO based models.Comment: 4 figures, 4 tables, 31 references, conference proceeding
First impressions: A survey on vision-based apparent personality trait analysis
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Personality analysis has been widely studied in psychology, neuropsychology, and signal processing fields, among others. From the past few years, it also became an attractive research area in visual computing. From the computational point of view, by far speech and text have been the most considered cues of information for analyzing personality. However, recently there has been an increasing interest from the computer vision community in analyzing personality from visual data. Recent computer vision approaches are able to accurately analyze human faces, body postures and behaviors, and use these information to infer apparent personality traits. Because of the overwhelming research interest in this topic, and of the potential impact that this sort of methods could have in society, we present in this paper an up-to-date review of existing vision-based approaches for apparent personality trait recognition. We describe seminal and cutting edge works on the subject, discussing and comparing their distinctive features and limitations. Future venues of research in the field are identified and discussed. Furthermore, aspects on the subjectivity in data labeling/evaluation, as well as current datasets and challenges organized to push the research on the field are reviewed.Peer ReviewedPostprint (author's final draft
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