On-line print-defect detecting in an incremental subspace learning framework

2010 ◽  
Author(s):  
Xiaogang Sun ◽  
Bin Chen ◽  
Liang Zhang
Keyword(s):  
Subspace Learning ◽  
Learning Framework ◽  
On Line

On‐line print‐defect detecting in an incremental subspace learning framework

Sensor Review ◽  
2011 ◽  
Vol 31 (2) ◽  
pp. 138-143 ◽  
Author(s):  
Xiaogang Sun ◽  
Liang Zhang ◽  
Bin Chen
Keyword(s):  
Subspace Learning ◽  
Learning Framework ◽  
On Line

scholarly journals Vehicle Counting in Video Sequences: An Incremental Subspace Learning Approach

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2848 ◽  
Author(s):  
Leonel Rosas-Arias ◽  
Jose Portillo-Portillo ◽  
Aldo Hernandez-Suarez ◽  
Jesus Olivares-Mercado ◽  
Gabriel Sanchez-Perez ◽  
...  
Keyword(s):  
Principal Component ◽  
Subspace Learning ◽  
Transport Systems ◽  
Video Sequences ◽  
Projected Image ◽  
The Road ◽  
Illumination Changes ◽  
On Line ◽  
Incremental Learning Method ◽  
On Line Learning

The counting of vehicles plays an important role in measuring the behavior patterns of traffic flow in cities, as streets and avenues can get crowded easily. To address this problem, some Intelligent Transport Systems (ITSs) have been implemented in order to count vehicles with already established video surveillance infrastructure. With this in mind, in this paper, we present an on-line learning methodology for counting vehicles in video sequences based on Incremental Principal Component Analysis (Incremental PCA). This incremental learning method allows us to identify the maximum variability (i.e., motion detection) between a previous block of frames and the actual one by using only the first projected eigenvector. Once the projected image is obtained, we apply dynamic thresholding to perform image binarization. Then, a series of post-processing steps are applied to enhance the binary image containing the objects in motion. Finally, we count the number of vehicles by implementing a virtual detection line in each of the road lanes. These lines determine the instants where the vehicles pass completely through them. Results show that our proposed methodology is able to count vehicles with 96.6% accuracy at 26 frames per second on average—dealing with both camera jitter and sudden illumination changes caused by the environment and the camera auto exposure.

A Pareto-Based Sparse Subspace Learning Framework

2019 ◽  
Vol 49 (11) ◽  
pp. 3859-3872
Author(s):  
Juanjuan Luo ◽  
Licheng Jiao ◽  
Fang Liu ◽  
Shuyuan Yang ◽  
Wenping Ma
Keyword(s):  
Subspace Learning ◽  
Learning Framework

Kalman Filter Control Embedded into the Reinforcement Learning Framework

2004 ◽  
Vol 16 (3) ◽  
pp. 491-499 ◽  
Author(s):  
István Szita ◽  
András Lőrincz
Keyword(s):  
Optimal Control ◽  
Kalman Filter ◽  
Reinforcement Learning ◽  
Learning Rule ◽  
Slight Modification ◽  
Linear Quadratic ◽  
Filter Model ◽  
Learning Framework ◽  
Value Estimation ◽  
On Line

There is a growing interest in using Kalman filter models in brain modeling. The question arises whether Kalman filter models can be used on-line not only for estimation but for control. The usual method of optimal control of Kalman filter makes use of off-line backward recursion, which is not satisfactory for this purpose. Here, it is shown that a slight modification of the linear-quadratic-gaussian Kalman filter model allows the on-line estimation of optimal control by using reinforcement learning and overcomes this difficulty. Moreover, the emerging learning rule for value estimation exhibits a Hebbian form, which is weighted by the error of the value estimation.

A shared-subspace learning framework for multi-label classification

2010 ◽  
Vol 4 (2) ◽  
pp. 1-29 ◽  
Author(s):  
Shuiwang Ji ◽  
Lei Tang ◽  
Shipeng Yu ◽  
Jieping Ye
Keyword(s):  
Subspace Learning ◽  
Learning Framework

scholarly journals Inferring subgroup-specific driver genes from heterogeneous cancer samples via subspace learning with subgroup indication

2019 ◽  
Author(s):  
Jianing Xi ◽  
Xiguo Yuan ◽  
Minghui Wang ◽  
Ao Li ◽  
Xuelong Li ◽  
...  
Keyword(s):  
Ground Truth ◽  
Subspace Learning ◽  
Detection Methods ◽  
Supplementary Information ◽  
Driver Gene ◽  
Key Factors ◽  
Gene Detection ◽  
Driver Genes ◽  
Learning Framework ◽  
Mutation Data

AbstractMotivationDetecting driver genes from gene mutation data is a fundamental task for tumorigenesis research. Due to the fact that cancer is a heterogeneous disease with various subgroups, subgroup-specific driver genes are the key factors in the development of precision medicine for heterogeneous cancer. However, the existing driver gene detection methods are not designed to identify subgroup specificities of their detected driver genes, and therefore cannot indicate which group of patients is associated with the detected driver genes, which is difficult to provide specifically clinical guidance for individual patients.ResultsBy incorporating the subspace learning framework, we propose a novel bioinformatics method called DriverSub, which can efficiently predict subgroup-specific driver genes in the situation where the subgroup annotations are not available. When evaluated by simulation datasets with known ground truth and compared with existing methods, DriverSub yields the best prediction of driver genes and the inference of their related subgroups. When we apply DriverSub on the mutation data of real heterogeneous cancers, we can observe that the predicted results of DriverSub are highly enriched for experimentally validated known driver genes. Moreover, the subgroups inferred by DriverSub are significantly associated with the annotated molecular subgroups, indicating its capability of predicting subgroup-specific driver genes.Availability and implementationThe source code is publicly available at https://github.com/JianingXi/DriverSub.Supplementary informationSupplementary data are available at Bioinformatics online.

scholarly journals E-learning megvalósítások tapasztalatai és eredményei az Eszterházy Károly Egyetemen

2021 ◽  
Author(s):  
Péter Antal
Keyword(s):  
Distance Learning ◽  
Research Group ◽  
Point Of View ◽  
Learning System ◽  
Knowledge Transmission ◽  
Learning Framework ◽  
University Experiences ◽  
E Learning ◽  
On Line ◽  
Framework System

A hagyományostól eltérő tudásközvetítés szerepe világszerte megnőtt. Ez egyrészt a meglévő kompetenciák, minőségi és tartalmi változásának köszönhető, másrészt a hagyományos oktatás tartalmi és strukturális rugalmatlanságából fakad. Annak ellenére, hogy a technika által támogatott tudásátadás néhány előnye kézenfekvőnek tűnhet, a távoktatás alkalmazásának mindenhol vannak korlátai. Az Eszterházy Károly Egyetemen 2010 óta használjuk a MOODLE távoktatási keretrendszert többkevesebb sikerrel. Az EFOP-3.4.3-16-2016 pályázat keretein belül létrejött egy kutatócsoport, amelynek feladata az interaktív, online kurzusokhoz kapcsolódóan a tanulási eredmények monitorozását lehetővé tévő eszközök, alkalmazások kísérleti beépítése illetve újak kifejlesztése. Ennek egyik része az a vizsgálat, amely a felhasználók elégedettségét (tanár, diák) és kompetenciáját méri. A koronavírus járvány okozta hirtelen változások nyilvánvalóvá tették, hogy mind a diákok mind a tanárok, egyetemi oktatók jó része nem készült fel a digitális oktatás kihívásaira, sem módszertani sem technikai szempontból. Előadásomban ennek a felmérésnek az eredményeiről szeretnék beszámolni. ----- The implementation of e-Learning solutions at the Eszterházy Károly University: experiences and results ----- The increasing global role of non-traditional knowledge transmission methods is partly due to the quality and content-based modifications of existing competences along with the inflexible content and structure of the traditional school. While technologically supported knowledge transmission has obvious benefits, distance learning generally has its own limitations. The MOODLE distance learning framework system was implemented at the Eszterházy Károly University in 2010 and has operated with varying success since then. In order to develop and integrate tools and applications facilitating the monitoring of learning outcomes related to interactive, on-line courses a research group was formed with the support of the EFOP-3.4.3-16-2016 project. The tasks of the research group included ascertaining the satisfaction and competence levels of the users (instructors and students) of the e-Learning system. The sudden changes brought on by the COVID-19 pandemic revealed that the majority of students and instructors were not prepared to respond to the challenges of digital instruction from a methodological or technological point of view. In my presentation, I will introduce the results of the abovementioned survey.

scholarly journals Interpretable Preference Learning: A Game Theoretic Framework for Large Margin On-Line Feature and Rule Learning

2019 ◽  
Vol 33 ◽  
pp. 4723-4730
Author(s):  
Mirko Polato ◽  
Fabio Aiolli
Keyword(s):  
Game Theory ◽  
Large Body ◽  
Rule Learning ◽  
Preference Learning ◽  
Learning Problem ◽  
Learning Framework ◽  
On Line ◽  
Game Theoretic ◽  
Classification Tasks ◽  
Zero Sum

A large body of research is currently investigating on the connection between machine learning and game theory. In this work, game theory notions are injected into a preference learning framework. Specifically, a preference learning problem is seen as a two-players zero-sum game. An algorithm is proposed to incrementally include new useful features into the hypothesis. This can be particularly important when dealing with a very large number of potential features like, for instance, in relational learning and rule extraction. A game theoretical analysis is used to demonstrate the convergence of the algorithm. Furthermore, leveraging on the natural analogy between features and rules, the resulting models can be easily interpreted by humans. An extensive set of experiments on classification tasks shows the effectiveness of the proposed method in terms of interpretability and feature selection quality, with accuracy at the state-of-the-art.

Supporting Pre-Service Teachers' Understanding and Use of Mobile Devices

2018 ◽  
pp. 160-177 ◽  
Author(s):  
Damian Maher
Keyword(s):  
Teacher Training ◽  
Mobile Devices ◽  
Professional Learning ◽  
Mobile Learning ◽  
Meaningful Learning ◽  
Learning Framework ◽  
On Line ◽  
On Line Learning

The use of mobile devices to support learning is increasing in schools and universities. This increase is having an impact on the types of pedagogies that are supporting learning. This chapter explores the use of mobile devices to support pre-service teachers' professional learning. A constructivist framework is used as a critical lens in conjunction with Howland, Jonassen and Marra's (2012) ‘meaningful learning' framework. Additionally, the notions of teacher training about and with mobile learning as described by Baran (2014) are drawn upon to help in understanding the field. The chapter begins with an exploration on-campus learning followed by learning in schools and finally, aspects of on-line learning.

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