Search results for "Machine"
showing 10 items of 2592 documents
Learning the relevant image features with multiple kernels
2009
This paper proposes to learn the relevant features of remote sensing images for automatic spatio-spectral classification with the automatic optimization of multiple kernels. The method consists of building dedicated kernels for different sets of bands, contextual or textural features. The optimal linear combination of kernels is optimized through gradient descent on the support vector machine (SVM) objective function. Since a na¨ive implementation is computationally demanding, we propose an efficient model selection procedure based on kernel alignment. The result is a weight — learned from the data — for each kernel where both relevant and meaningless image features emerge after training. E…
Multimodal biometric recognition systems using deep learning based on the finger vein and finger knuckle print fusion
2020
Recognition systems using multimodal biometrics attracts attention because they improve recognition efficiency and high-security level compared to the unimodal biometrics system. In this study, the authors present a secure multimodal biometrics recognition system based on the deep learning method that uses convolutional neural networks (CNNs). The authors propose two multimodal architectures using the finger knuckle print (FKP) and the finger vein (FV) biometrics with different levels of fusion: the features level fusion and scores level fusion. The features extraction for FKP and FV are performed using transfer learning CNN architectures: AlexNet, VGG16, and ResNet50. The key step aims to …
Recent advances in remote sensing image processing
2009
Remote sensing image processing is nowadays a mature research area. The techniques developed in the field allow many real-life applications with great societal value. For instance, urban monitoring, fire detection or flood prediction can have a great impact on economical and environmental issues. To attain such objectives, the remote sensing community has turned into a multidisciplinary field of science that embraces physics, signal theory, computer science, electronics, and communications. From a machine learning and signal/image processing point of view, all the applications are tackled under specific formalisms, such as classification and clustering, regression and function approximation…
Generating Hyperspectral Skin Cancer Imagery using Generative Adversarial Neural Network
2020
In this study we develop a proof of concept of using generative adversarial neural networks in hyperspectral skin cancer imagery production. Generative adversarial neural network is a neural network, where two neural networks compete. The generator tries to produce data that is similar to the measured data, and the discriminator tries to correctly classify the data as fake or real. This is a reinforcement learning model, where both models get reinforcement based on their performance. In the training of the discriminator we use data measured from skin cancer patients. The aim for the study is to develop a generator for augmenting hyperspectral skin cancer imagery. peerReviewed
Setting up of a machine learning algorithm for the identification of severe liver fibrosis profile in the general US population cohort
2022
Background: The progress of digital transformation in clinical practice opens the door to transforming the current clinical line for liver disease diagnosis from a late-stage diagnosis approach to an early-stage based one. Early diagnosis of liver fibrosis can prevent the progression of the disease and decrease liver-related morbidity and mortality. We developed here a machine learning (ML) algorithm containing standard parameters that can identify liver fibrosis in the general US population.Materials and methods: Starting from a public database (National Health and Nutrition Examination Survey, NHANES), representative of the American population with 7265 eligible subjects (control populati…
A Cognitive Framework for Learning by Imitation
2005
Representation, Recognition and Generation of Actions in the Context of Imitation Learning
2006
The paper deals with the development of a cognitive architecture for learning by imitation in which a rich conceptual representation of the observed actions is built. We adopt the paradigm of conceptual spaces, in which static and dynamic entities are employed to efficiently organize perceptual data, to recognize positional relations, to learn movements from human demonstration and to generate complex actions by combining and sequencing simpler ones. The aim is to have a robotic system able to effectively learn by imitation and which has the capabilities of deeply understanding the perceived actions to be imitated. Experimentation has been performed on a robotic system composed of a PUMA 20…
Alternatīvas neironu mašīntulkošanas arhitektūras
2020
Pētījuma mērķis: Izpētīt alternatīvas mašīntulkošanas arhitektūras, pielietojot jaunas pieejas, lai izveidotu angļu-latviešu tulkotāju un apvienot attēlu ar teikumu matricu vienā modelī. Nozīmīgākie rezultāti: Darba ietvaros tika uztrēnēti dažādi mašīntulkotāji. Viens balstās uz «transformer» arhitektūru. Šis modeļis uztrenēts pilnīgi no jauna. Otrs mašīntulkotājs balstās uz pretrenētiem modeliem. Darbā tika salīdzinātas dažādas pieejas un tika izvēlēts labākais pretrenēts modelis XLM-R, uz kura bāzes izveidots tulkotājs. Pirmajā tulkotājā iegūtie tulkojuma rezultāti ir pietiekami precīzi un gramatiski pareizi. Taču šis tulkotājs pielāgots politiski vai juridiski virzītam tekstam, jo uztren…
"Table 7" of "Measurement of D*+/- meson production in jets from pp collisions at sqrt(s) = 7 TeV with the ATLAS detector"
2012
Comparison of the reconstructed Z distribution with the reweighted Monte Carlo prediction.
Estimating feature discriminant power in decision tree classifiers
1995
Feature Selection is an important phase in pattern recognition system design. Even though there are well established algorithms that are generally applicable, the requirement of using certain type of criteria for some practical problems makes most of the resulting methods highly inefficient. In this work, a method is proposed to rank a given set of features in the particular case of Decision Tree classifiers, using the same information generated while constructing the tree. The preliminary results obtained with both synthetic and real data confirm that the performance is comparable to that of sequential methods with much less computation.