Search results for "Machine learning"
showing 10 items of 1464 documents
Principal Component and Neural Network Analyses of Face Images: What Can Be Generalized in Gender Classification?
1998
We present an overview of the major findings of the principal component analysis (pca) approach to facial analysis. In a neural network or connectionist framework, this approach is known as the linear autoassociator approach. Faces are represented as a weighted sum of macrofeatures (eigenvectors or eigenfaces) extracted from a cross-product matrix of face images. Using gender categorization as an illustration, we analyze the robustness of this type of facial representation. We show that eigenvectors representing general categorical information can be estimated using a very small set of faces and that the information they convey is generalizable to new faces of the same population and to a l…
DAE-GP
2020
Estimation of distribution genetic programming (EDA-GP) algorithms are metaheuristics where sampling new solutions from a learned probabilistic model replaces the standard mutation and recombination operators of genetic programming (GP). This paper presents DAE-GP, a new EDA-GP which uses denoising autoencoder long short-term memory networks (DAE-LSTMs) as probabilistic model. DAE-LSTMs are artificial neural networks that first learn the properties of a parent population by mapping promising candidate solutions to a latent space and reconstructing the candidate solutions from the latent space. The trained model is then used to sample new offspring solutions. We show on a generalization of t…
Scatter Search for the Point-Matching Problem in 3D Image Registration
2008
Scatter search is a population-based method that has recently been shown to yield promising outcomes for solving combinatorial and nonlinear optimization problems. Based on formulations originally proposed in the 1960s for combining decision rules and problem constraints, such as the surrogate constraint method, scatter search uses strategies for combining solution vectors that have proved effective in a variety of problem settings. We present a scatter-search implementation designed to find high-quality solutions for the 3D image-registration problem, which has many practical applications. This problem arises in computer vision applications when finding a correspondence or transformation …
Inferring Learning Strategies from Cultural Frequency Data
2015
Social learning has been identified as one of the fundamentals of culture and therefore the understanding of why and how individuals use social information presents one of the big questions in cultural evolution. To date much of the theoretical work on social learning has been done in isolation of data. Evolutionary models often provide important insight into which social learning strategies are expected to have evolved but cannot tell us which strategies human populations actually use. In this chapter we explore how much information about the underlying learning strategies can be extracted by analysing the temporal occurrence or usage patterns of different cultural variants in a population…
Set Membership (In) Validation of nonlinear positive models for biological systems
2006
The complexity of biology needs quantitative tools in order to support and validate biologists intuition and traditional qualitative descriptions. In this paper, Nonlinear Positive models with constraints for biological systems are validated/invalidated in a worst-case deterministic setting. These models are usefull for the analysis of the DNA and RNA evolution and for the description of the population dynamics of viruses and bacteria. The conditional central estimate and the Uncertainty Intervals are determined in order to validate/invalidate the model. The effectiveness of the proposed procedure has been illustrated by means of simulation experiments.
Smartphone data analysis for human activity recognition
2017
In recent years, the percentage of the population owning a smartphone has increased significantly. These devices provide the user with more and more functions, so that anyone is encouraged to carry one during the day, implicitly producing that can be analysed to infer knowledge of the userâs context. In this work we present a novel framework for Human Activity Recognition (HAR) using smartphone data captured by means of embedded triaxial accelerometer and gyroscope sensors. Some statistics over the captured sensor data are computed to model each activity, then real-time classification is performed by means of an efficient supervised learning technique. The system we propose also adopts a …
Anomaly Detection in Dynamic Social Systems Using Weak Estimators
2009
Anomaly detection involves identifying observationsthat deviate from the normal behavior of a system. One ofthe ways to achieve this is by identifying the phenomena thatcharacterize “normal” observations. Subsequently, based on thecharacteristics of data learned from the “normal” observations,new observations are classified as being either “normal” or not.Most state-of-the-art approaches, especially those which belongto the family parameterized statistical schemes, work under theassumption that the underlying distributions of the observationsare stationary. That is, they assume that the distributions thatare learned during the training (or learning) phase, thoughunknown, are not time-varyin…
Training machine learning models with synthetic data improves the prediction of ventricular origin in outflow tract ventricular arrhythmias
2022
In order to determine the site of origin (SOO) in outflow tract ventricular arrhythmias (OTVAs) before an ablation procedure, several algorithms based on manual identification of electrocardiogram (ECG) features, have been developed. However, the reported accuracy decreases when tested with different datasets. Machine learning algorithms can automatize the process and improve generalization, but their performance is hampered by the lack of large enough OTVA databases. We propose the use of detailed electrophysiological simulations of OTVAs to train a machine learning classification model to predict the ventricular origin of the SOO of ectopic beats. We generated a synthetic database of 12-l…
Absolute of Relative? A New Approach to Building Feature Vectors For Emotion Tracking In Music
2013
It is believed that violation of or conformity to expectancy when listening to music is one of the main sources of musical emotion. To address this, we test a new way of building feature vectors and representing features within the vector for the machine learning approach to continuous emotion tracking systems. Instead of looking at the absolute values for specific features, we concentrate on the average value of that feature across the whole song and the difference between that and the absolute value for a particular sample. To test this “relative” representation, we used a corpus of popular music with continuous labels on the arousalvalence space. The model consists of a Support Vector Re…
Classification of Solutions to the Minimum Energy Problem in One Dimensional Sensor Networks
2016
We classify of the minimum energy problem in one dimensional wireless sensor networks for the data transmission cost matrix which is a power function of the distance between transmitter and receiver with any real exponent. We show, how these solutions can be utilized to solve the minimum energy problem for the data transmission cost matrix which is a linear combination of two power functions. We define the minimum energy problem in terms of the sensors signal power, transmission time and capacities of transmission channels. We prove, that for the point-to-point data transmission method utilized by the sensors in the physical layer, when the transmitter adjust the power of its radio signal t…