Search for tag: "dimensionality"

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09:47 Anis Hamadouche Presentation - SSPD 2021 Anis Hamadouche Presentation - SSPD 2021	Anis Hamadouche Presentation - SSPD 2021 Presented at the Sensor Signal Processing for Defence Conference (SSPD) 2021Presentation: "Approximate Proximal-Gradient Methods" Speaker: Anis Hamadouche, Heriot-Watt… sspd 2021 error bounds rate convergence errors table analysis approximations precision algorithm function gradient bound assumptions problems optimization application step vector dimensionality From Joseph Burchell on October 21st, 2021 0 likes 10 plays 0 Licence Type All rights reserved Language English Date Created September 15th, 2021
03:00 Edossa Merga Terefe EVA Talk Preview Edossa Merga Terefe EVA Talk Preview	Edossa Merga Terefe EVA Talk Preview This talk has captions. You can remove these by pressing CC on the video toolbar. Name: Edossa Merga Terefe Talk Title: Extremal Random Forests Abstract: Methods from statistics and machine… icms eva theory solution model data extremal random forests methods statistics machine learning quantiles function quantile space predictor models algorithm idea range dimensionality picture level variable response detail conference cantilever philip levels erf competitor From Anna Munro on May 25th, 2021 0 likes 19 plays 0 Licence Type All rights reserved Language English
08:32 ML2: Dimensionality Reduction ML2: Dimensionality Reduction	ML2: Dimensionality Reduction In this second video, we motivate why it is useful to reduce the dimensionality of your data, and describe how this can be done linearly, using a transformation matrix. We describe a setup whereby we… matrix reconstruction squiggle points dimensionality transpose transformation point column dataset reconstructions distance era columns dimensions inverse rows dimension x. distances From Elliot Crowley on November 5th, 2020 0 likes 14 plays 0
06:35 MLP Lecture 07 - Clip 06 - Multiple Feature Maps MLP Lecture 07 - Clip 06 - Multiple Feature Maps	MLP Lecture 07 - Clip 06 - Multiple Feature Maps Machine Learning Practical (MLP) Lecture 07, Clip 06 / 09. machine learning deep learning times feature maps channels input image kernel size units kernels channel output matrices parameters 3-dimensions dimension dimensionality connections bias capital From Pavlos Andreadis on November 2nd, 2020 1 likes 306 plays 0 Course Code INFR11132 Licence Type All rights reserved Language English
05:31 Unsupervised Learning Unsupervised Learning	Unsupervised Learning Unsupervised Learning hds dataset learning cluster clusters features pca component algorithm instance components variance direction reduction assignment point dimensions words dimensionality centers points From Dimitrios Doudesis on June 8th, 2020 0 likes 249 plays 0 Course Code GLHE11086 Licence Type Creative Commons - Attribution Share A Like
04:53 Sampling Sampling	Sampling Sampling - Week 4 homogeneous computer data predictive analytics micromasters presence trend random sampling sample top-level domains techniques dimensionality positive point emphasize ensure caption complete approach hiv furthest selecting hiv positive effects dataset distance training scientific method observation data set length algorithms limited number replacement From Calum Macphail on April 5th, 2019 0 likes 114 plays 0 Course Code PA1.2x Evaluation of Predictive Modelling Licence Type Creative Commons - Attribution Share A Like Language English
01:59 Pros and cons of dimensionality reduction Pros and cons of dimensionality reduction	Pros and cons of dimensionality reduction Pros and cons of dimensionality reduction iaml pca space classification bayes dimensionality dimensions lot reason harm classes goal sets algorithms class downside upside assumption independence classifier classifiers From Nigel Goddard on September 25th, 2016 1 likes 2,343 plays 0 Licence Type Creative Commons - Attribution
03:56 How many principal components to use How many principal components to use	How many principal components to use How many principal components to use iaml pca variance eigenvalues sum dimensions lambda eigenvectors point dimensionality graph space decrease elbow inflection plot means scri sentences numbers sense humans From Nigel Goddard on September 25th, 2016 1 likes 1,950 plays 0 Licence Type Creative Commons - Attribution
02:59 Why we maximize variance in PCA Why we maximize variance in PCA	Why we maximize variance in PCA Why we maximize variance in PCA iaml pca dimension points space variance projection distances point dimensions e. dimensionality step weight cases channel algorithms basis height colors dataset thing From Nigel Goddard on September 25th, 2016 2 likes 2,693 plays 0 Licence Type Creative Commons - Attribution
03:59 Tackling the curse of dimensionality Tackling the curse of dimensionality	Tackling the curse of dimensionality Tackling the curse of dimensionality iaml pca lot attributes regions assumption space attribute independence histograms observations domain features dimensionality symmetry thing cells neighboring probability distributions smoothness ability From Nigel Goddard on September 25th, 2016 1 likes 2,339 plays 0 Licence Type Creative Commons - Attribution
02:47 The curse of dimensionality The curse of dimensionality	The curse of dimensionality The curse of dimensionality iaml pca space regions observations examples points attribute training times problem dimensionality lots dataset decision curse inference zeros cells lot accounts dimensions From Nigel Goddard on September 25th, 2016 2 likes 2,330 plays 0 Licence Type Creative Commons - Attribution
03:54 Data manifolds in high-dimensional spaces Data manifolds in high-dimensional spaces	Data manifolds in high-dimensional spaces Data manifolds in high-dimensional spaces iaml pca space digits dimensionality pixels structure text pen digit examples things binary bitmaps point points lots attribute dimensions images rendering stroke From Nigel Goddard on September 25th, 2016 2 likes 2,328 plays 0 Licence Type Creative Commons - Attribution
02:19 Pros and cons of nearest-neighbor methods Pros and cons of nearest-neighbor methods	Pros and cons of nearest-neighbor methods Pros and cons of nearest-neighbor methods iaml nearest neighbours training algorithm examples testing trees decision sets operations instance comparisons attributes lots values instances thing test estimates dimensionality times bit From Nigel Goddard on September 21st, 2016 1 likes 3,217 plays 0 Licence Type Creative Commons - Attribution

Search for tag: "dimensionality"

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