#statisticallearningtheory نتائج البحث

On the beauty of logloss/perplexity metric for model selection (which model is more decisive) and its relation to KL Divergence and conditional entropy--better metric than CCR or when CCRs are the same for two models. #statisticallearningtheory #machinelearning

MonaJalal_'s tweet image. On the beauty of logloss/perplexity metric for model selection (which model is more decisive) and its relation to KL Divergence and conditional entropy--better metric than CCR or when CCRs are the same for two models.
#statisticallearningtheory 
#machinelearning
MonaJalal_'s tweet image. On the beauty of logloss/perplexity metric for model selection (which model is more decisive) and its relation to KL Divergence and conditional entropy--better metric than CCR or when CCRs are the same for two models.
#statisticallearningtheory 
#machinelearning
MonaJalal_'s tweet image. On the beauty of logloss/perplexity metric for model selection (which model is more decisive) and its relation to KL Divergence and conditional entropy--better metric than CCR or when CCRs are the same for two models.
#statisticallearningtheory 
#machinelearning
MonaJalal_'s tweet image. On the beauty of logloss/perplexity metric for model selection (which model is more decisive) and its relation to KL Divergence and conditional entropy--better metric than CCR or when CCRs are the same for two models.
#statisticallearningtheory 
#machinelearning

Most discriminative SSL methods optimise a biased estimate of the risk that can hurt empirical performance. Our approach removes this bias and provides simple theoretical guarantees on the safeness of the modified methods. (4/8) #StatisticalLearningTheory

HugoSchmutz2's tweet image. Most discriminative SSL methods optimise a biased estimate of the risk that can hurt empirical performance. Our approach removes this bias and provides simple theoretical guarantees on the safeness of the modified methods. (4/8) #StatisticalLearningTheory

Equal odds for joint loss that combines fairness and accuracy for decoupled classifiers for majority and minority groups #FAT2018 @fatconference @Microsoft #MachineLearning #statisticallearningtheory By Nicole Immorlica

MonaJalal_'s tweet image. Equal odds for joint loss that combines fairness and accuracy for decoupled classifiers for majority and minority groups
#FAT2018 @fatconference @Microsoft 
#MachineLearning 
#statisticallearningtheory  
By Nicole Immorlica

Hot topic - recommended #textbook with innovative & clear concept: Machine Learning. A Practical Approach on the Statistical Learning Theory. bit.ly/2EkVHNh >11k downloads #MachineLearning #StatisticalLearningTheory #DataScience @ponti3

SpringerCompSci's tweet image. Hot topic - recommended #textbook with innovative & clear concept: Machine Learning. A Practical Approach on the Statistical Learning Theory. bit.ly/2EkVHNh  >11k downloads #MachineLearning #StatisticalLearningTheory #DataScience 
@ponti3

:( 4,2 voor tentamen gehaald, even kijken of ik er nog wat bij kan kletsen... #StatisticalLearningTheory #tentamens


Check out our new article by @sadimanna Statistical Learning Theory Part 4 This is the 4th article in the Statistical Learning Theory series. Please give it a read and don't forget to give your valuable feedback. link.medium.com/oPjoCBZ3ghb



On generalization bounds for deep networks based on loss surface implicit regularization deepai.org/publication/on… by Masaaki Imaizumi et al. #StatisticalLearningTheory #LossFunction


Emp-VC-dim is a sample-dependent measure of (learning/sample) complexity. Kinda a worst-case #RademacherAverage, from #statisticallearningtheory. It works great in sampling-based #approximationalgorithms for #datamining. MNI is a anti-monotone frequency measure for #subgraphs.


Alles goed geoefend, het moet goed komen nu! Nog even een uurtje ontspannen en dan gaan we er voor! #StatisticalLearningTheory #tentamens


Visual Recognition with Deep Learning from Biased Image Datasets deepai.org/publication/vi… by Robin Vogel et al. #Statistics #StatisticalLearningTheory


Ik heb alles maar dan ook alles gedaan voor #StatisticalLearningTheory. Het boek doorgelezen, alle opgaven gemaakt, alle tentamens gemaakt... maar het wil maar niet lukken. Dadelijk nog de samenvatting doorlezen en dan hopen op het beste morgen #tentamens


I just published Analyzing Finite Function Classes in Statistical Learning Theory: Consistency, Rates, and Bounds… link.medium.com/7YF8CEL3oEb #StatisticalLearningTheory #FiniteFunctionClasses #Consistency


Most discriminative SSL methods optimise a biased estimate of the risk that can hurt empirical performance. Our approach removes this bias and provides simple theoretical guarantees on the safeness of the modified methods. (4/8) #StatisticalLearningTheory

HugoSchmutz2's tweet image. Most discriminative SSL methods optimise a biased estimate of the risk that can hurt empirical performance. Our approach removes this bias and provides simple theoretical guarantees on the safeness of the modified methods. (4/8) #StatisticalLearningTheory

🤔 Wondering what Statistical Learning Theory is? 🤓 🤓 🤓 🤓 🤓 🤓 We got you covered! 🤩 Check out this AI glossary article and get smart: 🤓 🤓 🤓 🤓 🤓 🤓 deepai.org/machine-learni… 🤩 #AIforAll #AI #StatisticalLearningTheory


From the Machine Learning & Data Science glossary: Statistical Learning Theory deepai.org/machine-learni… #StatisticalHypothesisTesting #StatisticalLearningTheory


From the Machine Learning & Data Science glossary: Statistical Learning Theory deepai.org/machine-learni… #StatisticalHypothesisTesting #StatisticalLearningTheory


From the Machine Learning & Data Science glossary: Statistical Learning Theory deepai.org/machine-learni… #ProbabilityDistribution #StatisticalLearningTheory


From the Machine Learning & Data Science glossary: Statistical Learning Theory deepai.org/machine-learni… #DeepLearning #StatisticalLearningTheory


Learning deterministic hydrodynamic equations from stochastic active particle dynamics deepai.org/publication/le… by @surimk92 et al. #StatisticalLearningTheory #ComputerScience


On generalization bounds for deep networks based on loss surface implicit regularization deepai.org/publication/on… by Masaaki Imaizumi et al. #StatisticalLearningTheory #LossFunction


Visual Recognition with Deep Learning from Biased Image Datasets deepai.org/publication/vi… by Robin Vogel et al. #Statistics #StatisticalLearningTheory


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