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A New Graph Constructor for Semi-supervised Discriminant Analysis via Group Sparsity
2011
2011 Sixth International Conference on Image and Graphics
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Inspired by the advances of compressive sensing, we propose a novel graph construction method via group sparsity,which means to constrain the reconstruct data to be sparse for each sample, and constrain ...
This paper studies the Semi-supervised Discriminant Analysis (SDA) algorithm, which aims at dimensionality reduction utilizing both limited labeled data and abundant unlabeled data. ...
ACKNOWLEDGMENT We would like to thank anonymous reviewers for their comments. ...
doi:10.1109/icig.2011.82
dblp:conf/icig/GaoZY11
fatcat:ju6j36znsnggzm7kv2iguyxjlu
Detection of Malicious Software by Analyzing Distinct Artifacts Using Machine Learning and Deep Learning Algorithms
2021
Electronics
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Malware analysis and prevention methods are increasingly becoming necessary for computer systems connected to the Internet. ...
Prominent features are extracted using Minimum Redundancy and Maximum Relevance (mRMR) and Analysis of Variance (ANOVA). ...
The training process can be either supervised, unsupervised, and semi-supervised. Supervised feature selection determines feature relevance by evaluating the correlation of attributes with the class. ...
doi:10.3390/electronics10141694
fatcat:wj5oa566gzcjldfq62kqnc3mrm
Extracting event and their relations from texts: A survey on recent research progress and challenges
2020
AI Open
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A B S T R A C T Event is a common but non-negligible knowledge type. ...
This paper summaries some constructed event-centric knowledge graphs and the recent typical approaches for event and event relation extraction, besides task description, widely used evaluation datasets ...
curated semi-structured sources. ...
doi:10.1016/j.aiopen.2021.02.004
fatcat:qxbcmk55vzcb5nznhgfgwrbe4u
Learning with Kernels and Logical Representations
[chapter]
2008
Lecture Notes in Computer Science
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The relational representation of data and background knowledge are used to form a kernel function, enabling us to subsequently apply a number of kernel-based statistical learning algorithms. ...
In this chapter, we describe a view of statistical learning in the inductive logic programming setting based on kernel methods. ...
A special thanks goes to Alessio Ceroni, Fabrizio Costa, Kristian Kersting, Sauro Menchetti, and Jan Ramon, who helped us in numerous occasions with fruitful discussions. ...
doi:10.1007/978-3-540-78652-8_3
fatcat:tifoqknicffuxe5kgaup6p3tc4
Opportunities and obstacles for deep learning in biology and medicine
2018
Journal of the Royal Society Interface
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Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. ...
Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. ...
We thank Aaron Sheldon, who contributed text but did not formally approve the manuscript; Anna Greene for a careful proofreading of the manuscript in advance of the first submission; Sebastian Raschka ...
doi:10.1098/rsif.2017.0387
pmid:29618526
pmcid:PMC5938574
fatcat:65o4xmp53nc6zmj37srzuht6tq
Mining the Semantic Web
2012
Data mining and knowledge discovery
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This is intended to show the breadth and general potential of this exiting new research and application area for data mining. ...
From another perspective, the more expressive structured representations open up new opportunities for data mining, knowledge extraction and machine learning techniques. ...
For the area of social network analysis this could mean finding similar persons to identify sub-groups of people. ...
doi:10.1007/s10618-012-0253-2
fatcat:qhkv27bbkvba3mlolhixrepioq
Graph Neural Networks for Natural Language Processing: A Survey
[article]
2021
arXiv
pre-print
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As a result, thereis a surge of interests in developing new deep learning techniques on graphs for a large numberof NLP tasks. ...
We propose a new taxonomy of GNNs for NLP, whichsystematically organizes existing research of GNNs for NLP along three axes: graph construction,graph representation learning, and graph based encoder-decoder ...
Semi-supervised entity alignment via joint
knowledge embedding model and cross-graph model. ...
arXiv:2106.06090v1
fatcat:zvkhinpcvzbmje4kjpwjs355qu
Semantic Similarity from Natural Language and Ontology Analysis
2015
Synthesis Lectures on Human Language Technologies
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2017 pre-print arXiv:1704.05295v1 fatcat:oivxz6re5fgupdt3p5i3skko7a
The two state-of-the-art approaches for estimating and quantifying semantic similarities/relatedness of semantic entities are presented in detail: the first one relies on corpora analysis and is based ...
Beyond a simple inventory and categorization of existing measures, the aim of this monograph is to convey novices as well as researchers of these domains towards a better understanding of semantic similarity ...
These measures rely on algorithms designed for graph analysis which are generally used in a straightforward manner. ...
doi:10.2200/s00639ed1v01y201504hlt027
fatcat:y3tbtmwwqbhydeuaf2dlqk62ui
Multi-word Entity Classification in a Highly Multilingual Environment
2017
Proceedings of the 13th Workshop on Multiword Expressions (MWE 2017)
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The program also included a panel discussion on the future directions of the MWE community and the SIGLEX Section. ...
We would like to thank the members of the program committee for the timely reviews, authors for their valuable contributions, shared task organizers, annotators, and system developers for their hard work ...
Juan de la Cierva formación grant (FJCI-2014-22853), and by a postdoctoral fellowship granted by the Galician Government (POS-A/2013/191). ...
doi:10.18653/v1/w17-1702
dblp:conf/mwe/ChesneyJSP17
fatcat:bv7aavgth5eurmzuphuowtuuhq
Graph mining on static, multiplex and attributed networks
[article]
2021
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Relational data poses challenges for information extraction and knowledge discovery due to its web scale size, extreme sparsity, multimodality, the presence of spatial autocorrelation and heterogeneity ...
Graph structured data is pervasive and generated by online human interactions at an unprece- dented velocity. ...
network with a downstream semi-supervised model. ...
doi:10.7488/era/1498
fatcat:kmqs4lnzmzam7a7q53aic7nf3y
Learning Capacity in Simulated Virtual Neurological Procedures
2020
Journal of WSCG
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ACKNOWLEDGEMENTS The authors would like to thank Oana Rotaru-Orhei for her comments and the three anonymous reviewers for their insightful suggestions. ...
This work was partially supported by a grant of Ministry of Research and Innovation, CNCS -UEFISCDI, project number PN-III-P4-ID-PCE-2016-0842, within PNCDI III. ...
The Linear Discriminant Analysis is applied as a supervised data reduction technique. ...
doi:10.24132/csrn.2020.3001.13
fatcat:uytlm7nytrhmnk553ellfhl54a
Data Mining Algorithms for Decentralized Fault Detection and Diagnostic in Industrial Systems
[article]
2020
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For small-scale sensor networks, it is reasonable to assume that all measurements are available at a central location (sink) where fault predictions are made. ...
Once a fault has been detected pinpointing the type of fault is needed for purposes of fault mitigation and returning to normal process operation. This is known as Fault Diagnosis. ...
The semi-supervised model f ss is retrained upon receipt of a new batch of labeled data by solving (4.8). ...
doi:10.34944/dspace/1320
fatcat:in4zukomcbfmlkt3xgbshs6n3m
Learning with Kernels and Logical Representations
[chapter]
Inductive Logic Programming
Preserved Fulltext
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The relational representation of data and background knowledge are used to form a kernel function, enabling us to subsequently apply a number of kernel-based statistical learning algorithms. ...
In this chapter, we describe a view of statistical learning in the inductive logic programming setting based on kernel methods. ...
A special thanks goes to Alessio Ceroni, Fabrizio Costa, Kristian Kersting, Sauro Menchetti, and Jan Ramon, who helped us in numerous occasions with fruitful discussions. ...
doi:10.1007/978-3-540-78469-2_1
dblp:conf/ilp/Frasconi07
fatcat:mb775tlbqzeg7owzjrhnoq76wm
Distance and kernel based learning over composite representations
2008
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Un remerciement spécial va à tous les autres membres du Groupe d'Intelligence Artificielle grâce auxquels j'ai pu évolué dans un environnement stimulant, en particulier pour toutes les (longues) discussions ...
Alexandros Kalousis pour m'avoir accepté comme doctorant au sein du Groupe d'Intelligence Artificielle de l'Université de Genève. ...
Remerciements J'ai maintenant le plaisir et la joie de saisir cette occasion pour remercier toutes les personnes qui m'ont accompagné et m'ont soutenu au cours des années de travail qui ont conduit à cette ...
doi:10.13097/archive-ouverte/unige:5483
fatcat:fkmt5sko2bek5mmucwiegg45tq
Multi-Modal Technology for User Interface Analysis including Mental State Detection and Eye Tracking Analysis
2017
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For video data retrieval, we use a basic webcam. We investigate combinations of observation modalities to detect and extract affective and mental states. ...
We report on the recognition accuracy of basic emotions for each modality. ...
Semi-supervised quantitative evaluation We test the performance of semi-supervised learning using DTCN. The evaluation is based on a standard digit classification benchmark, MNIST. ...
doi:10.20381/ruor-20731
fatcat:5ayqafaobzeavgxrcznn7n42q4
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