About: Davide Anguita

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An Entity of Type : wikibase:Item, within Data Space : wikidata.demo.openlinksw.com associated with source document(s)

researcher

Attributes	Values
rdf:type	Item
description	forskare (sv) researcher (en) 연구원 (ko) 研究者 (ja) дослідник (uk) эрдэмтэн (mn) изилдөөчү (ky) 研究者 (zh) հետազոտող (hy) onderzoeker (nl)
rdfs:label	Davide Anguita (en) Davide Anguita (es) Davide Anguita (it) Davide Anguita (nl) Davide Anguita (sl) Davide Anguita (ast)
skos:prefLabel	Davide Anguita (en) Davide Anguita (es) Davide Anguita (it) Davide Anguita (nl) Davide Anguita (sl) Davide Anguita (ast)
name	Davide Anguita (en) Davide Anguita (es) Davide Anguita (it) Davide Anguita (nl) Davide Anguita (sl) Davide Anguita (ast)
Scopus author ID	wds:Q50597945-CC42CE9C-14A8-482B-B357-5ACC2A45FBC2
Scopus author ID	7004764744
given name	wds:Q50597945-380D3AB3-6C08-4414-9A39-1845C6623206
given name	Davide
instance of	wds:Q50597945-3CE09F35-4970-44F7-9BD6-CF4A8107BD69
instance of	Homo sapiens
zbMATH author ID	wds:Q50597945-F9A567F9-4794-49F6-9C55-56C3FEAD2884
zbMATH author ID	anguita.davide
occupation	wds:Q50597945-D09A464E-041A-44A7-9B88-86F7190EF97A
employer	wds:Q50597945-8DF0B86C-61FB-4D9D-AB26-3B5AE1D29ED4 wds:Q50597945-EE7A382E-E092-4946-A985-847BC17F254F
sex or gender	wds:Q50597945-62F9ECD6-8C1C-4E8F-B1B4-B37D48238F5B
VIAF ID	wds:Q50597945-bed77d0b-4922-49cf-a2d4-a3a9ca89c1ee
GND ID	wds:Q50597945-af7d78be-17bd-4c35-8472-687011f2298c
SBN author ID	wds:Q50597945-3ABEFBCC-5834-4F86-A6CB-9B0242BEE5AB
maintained by WikiProject	wds:Q50597945-E3DBE04A-D0E4-41F7-AC23-459754741D35
educated at	wds:Q50597945-32619A1C-DFB5-4FAE-9248-85AB3536E0F0
family name	wds:Q50597945-72E55A23-9576-443D-BEA0-D5400A243C6F
VIAF ID	http://viaf.org/viaf/2159154741635953110009
GND ID	https://d-nb.info/gnd/1175219134
occupation	researcher
employer	International Computer Science Institute Annalf of Faculty of Political Science of Genova
sex or gender	male
VIAF ID	2159154741635953110009
GND ID	1175219134
SBN author ID	LIGV016658
maintained by WikiProject	WikiProject Mathematics
educated at	Annalf of Faculty of Political Science of Genova
family name	Anguita
ORCID iD	wds:Q50597945-297FE504-4912-4F08-8B85-979D223289C7
Dimensions author ID	wds:Q50597945-DCE0EA3C-371D-4527-A260-4E5FF7F8F307
ORCID iD	https://orcid.org/0000-0001-7523-5291
ORCID iD	0000-0001-7523-5291
Dimensions author ID	015746525501.73
IRIS UNIGE author ID	wds:Q50597945-4E27C7E5-3DB4-4B51-8B2D-3C8F35CD802A
IRIS UNIGE author ID	02812
is about of	https://www.wikidata.org/wiki/Special:EntityData/Q50597945
is author of	A Learning Analytics Approach to Correlate the Academic Achievements of Students with Interaction Data from an Educational Simulator Big Data Analytics in the Cloud: Spark on Hadoop vs MPI/OpenMP on Beowulf Out-of-Sample Error Estimation: The Blessing of High Dimensionality A Learning Analytics Methodology to Profile Students Behavior and Explore Interactions with a Digital Electronics Simulator Unlabeled patterns to tighten Rademacher complexity error bounds for kernel classifiers A support vector machine classifier from a bit-constrained, sparse and localized hypothesis space Some results about the Vapnik-Chervonenkis entropy and the rademacher complexity Statistical Learning Theory and ELM for Big Social Data Analysis Local Rademacher Complexity: sharper risk bounds with and without unlabeled samples. Human Activity Recognition on Smartphones Using a Multiclass Hardware-Friendly Support Vector Machine Transition-Aware Human Activity Recognition Using Smartphones Test error bounds for classifiers: A survey of old and new results Human Activity Recognition on Smartphones with Awareness of Basic Activities and Postural Transitions Training Computationally Efficient Smartphone–Based Human Activity Recognition Models Using unsupervised analysis to constrain generalization bounds for support vector classifiers. Improved neural network for SVM learning. Learning Resource-Aware Classifiers for Mobile Devices: From Regularization to Energy Efficiency Support vector machines and strictly positive definite kernel: The regularization hyperparameter is more important than the kernel hyperparameters A Deep Connection Between the Vapnik–Chervonenkis Entropy and the Rademacher Complexity Learning algorithm for nonlinear support vector machines suited for digital VLSI Machine learning for wear forecasting of naval assets for condition-based maintenance applications Model selection for support vector machines: Advantages and disadvantages of the Machine Learning Theory Energy Load Forecasting Using Empirical Mode Decomposition and Support Vector Regression Fully Empirical and Data-Dependent Stability-Based Bounds In-sample and out-of-sample model selection and error estimation for support vector machines Global Rademacher Complexity Bounds: From Slow to Fast Convergence Rates In-sample Model Selection for Trimmed Hinge Loss Support Vector Machine In-sample model selection for Support Vector Machines Learning Hardware-Friendly Classifiers Through Algorithmic Stability Tikhonov, Ivanov and Morozov regularization for support vector machine learning Smartphone battery saving by bit-based hypothesis spaces and local Rademacher Complexities Maximal Discrepancy for Support Vector Machines Selecting the hypothesis space for improving the generalization ability of Support Vector Machines

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