#classifiers — Public Fediverse posts

@icing There's a thing called "the curse of dimensionality" and it applies to neural networks. I guess you could say that it's like a reverse Moore's Law but for neural nets. Basically, (and this is just my mostly-non technical explanation), neural nets are basically huge multi-dimensional classifiers and when you need to do backpropagation to train the net, it involves making small adjustments to localised areas of the classifier space. The problem (or curse) of having more dimensions is that it becomes harder and harder to localise the changes because every local space becomes closer to all the other points in every other subspace. This means exponentially higher training costs as these models scale.

At least that's as I understand it. I'm not a mathematician, but I have read plenty of stuff relating to machine learning over the years (since the 90s) and I think I've got the above right...

#MooresLaw #MachineLearning #Classifiers

@icing There's a thing called "the curse of dimensionality" and it applies to neural networks. I guess you could say that it's like a reverse Moore's Law but for neural nets. Basically, (and this is just my mostly-non technical explanation), neural nets are basically huge multi-dimensional classifiers and when you need to do backpropagation to train the net, it involves making small adjustments to localised areas of the classifier space. The problem (or curse) of having more dimensions is that it becomes harder and harder to localise the changes because every local space becomes closer to all the other points in every other subspace. This means exponentially higher training costs as these models scale.

At least that's as I understand it. I'm not a mathematician, but I have read plenty of stuff relating to machine learning over the years (since the 90s) and I think I've got the above right...

#MooresLaw #MachineLearning #Classifiers

@icing There's a thing called "the curse of dimensionality" and it applies to neural networks. I guess you could say that it's like a reverse Moore's Law but for neural nets. Basically, (and this is just my mostly-non technical explanation), neural nets are basically huge multi-dimensional classifiers and when you need to do backpropagation to train the net, it involves making small adjustments to localised areas of the classifier space. The problem (or curse) of having more dimensions is that it becomes harder and harder to localise the changes because every local space becomes closer to all the other points in every other subspace. This means exponentially higher training costs as these models scale.

At least that's as I understand it. I'm not a mathematician, but I have read plenty of stuff relating to machine learning over the years (since the 90s) and I think I've got the above right...

#MooresLaw #MachineLearning #Classifiers

@icing There's a thing called "the curse of dimensionality" and it applies to neural networks. I guess you could say that it's like a reverse Moore's Law but for neural nets. Basically, (and this is just my mostly-non technical explanation), neural nets are basically huge multi-dimensional classifiers and when you need to do backpropagation to train the net, it involves making small adjustments to localised areas of the classifier space. The problem (or curse) of having more dimensions is that it becomes harder and harder to localise the changes because every local space becomes closer to all the other points in every other subspace. This means exponentially higher training costs as these models scale.

At least that's as I understand it. I'm not a mathematician, but I have read plenty of stuff relating to machine learning over the years (since the 90s) and I think I've got the above right...

#MooresLaw #MachineLearning #Classifiers

@icing There's a thing called "the curse of dimensionality" and it applies to neural networks. I guess you could say that it's like a reverse Moore's Law but for neural nets. Basically, (and this is just my mostly-non technical explanation), neural nets are basically huge multi-dimensional classifiers and when you need to do backpropagation to train the net, it involves making small adjustments to localised areas of the classifier space. The problem (or curse) of having more dimensions is that it becomes harder and harder to localise the changes because every local space becomes closer to all the other points in every other subspace. This means exponentially higher training costs as these models scale.

At least that's as I understand it. I'm not a mathematician, but I have read plenty of stuff relating to machine learning over the years (since the 90s) and I think I've got the above right...

#MooresLaw #MachineLearning #Classifiers

Doctoral Thesis: Improving #bird #sound #classifiers for #passive #acoustic #monitoring In recent years, passive acoustic monitoring #PAM has emerged as a powerful tool for biodiversity assessment for vocalizing taxa such as birds, bats, amphibians and insects. helda.helsinki.fi/items/219f9a...

@inthehands There many ways of automating the process of classification, even when the number of features is very high (Decision Trees are one example). The current crop of machine-learning #classifiers are good at classification even when the important features (among all features) are not identified in advance. We can explain how these algorithms work, but not WHY they work in any particular example or in general. That means their suitability or reliability for any specific use case cannot be determined.

You are right. We can and should leave out the concept of “intelligence” entirely.

'A Comparative Evaluation of Quantification Methods', by Tobias Schumacher, Markus Strohmaier, Florian Lemmerich.

http://jmlr.org/papers/v26/21-0241.html

#classifiers #supervised #quantification

'An Optimal Transport Approach for Computing Adversarial Training Lower Bounds in Multiclass Classification', by Nicolas Garcia Trillos, Matt Jacobs, Jakwang Kim, Matthew Werenski.

http://jmlr.org/papers/v25/24-0268.html

#adversarial #regularization #classifiers

'Optimal Decision Tree and Adaptive Submodular Ranking with Noisy Outcomes', by Su Jia, Fatemeh Navidi, Viswanath Nagarajan, R. Ravi.

http://jmlr.org/papers/v25/23-1484.html

#adaptive #classifiers #optimal

Cost of false positives | Kellan Elliott-McCrea: Blog

Kevin Marks (q.v.) introduced me to Kellan’s Paradox of False Positives in Social Media, which predates the themes I explored in Billion Grains of Rice by 5+ years:

Imagine you’ve got a near perfect model for detecting spammers on Twitter. Say [that] Joe is (presumably hyperbolically) claiming 99% accuracy for his model. And for the moment we’ll imagine he is right. Even at 99% accuracy, that means this algorithm is going to be incorrectly flagging roughly 2 million tweets per day as spam that are actually perfectly legitimate.

https://laughingmeme.org//2011/07/23/cost-of-false-positives/

Via: https://bsky.app/profile/kevinmarks.com/post/3lefwdts3n225

⊞

#classifiers #ofcom #onlineHarms #onlineSafetyAct

'Estimating the Replication Probability of Significant Classification Benchmark Experiments', by Daniel Berrar.

http://jmlr.org/papers/v25/24-0158.html

#classifiers #replicability #hypothesis

'An Asymptotic Study of Discriminant and Vote-Averaging Schemes for Randomly-Projected Linear Discriminants', by Lama B. Niyazi, Abla Kammoun, Hayssam Dahrouj, Mohamed-Slim Alouini, Tareq Y. Al-Naffouri.

http://jmlr.org/papers/v25/22-1367.html

#classifiers #ensembles #en

'An Asymptotic Study of Discriminant and Vote-Averaging Schemes for Randomly-Projected Linear Discriminants', by Lama B. Niyazi, Abla Kammoun, Hayssam Dahrouj, Mohamed-Slim Alouini, Tareq Y. Al-Naffouri.

http://jmlr.org/papers/v25/22-1367.html

#classifiers #ensembles #en

'An Asymptotic Study of Discriminant and Vote-Averaging Schemes for Randomly-Projected Linear Discriminants', by Lama B. Niyazi, Abla Kammoun, Hayssam Dahrouj, Mohamed-Slim Alouini, Tareq Y. Al-Naffouri.

http://jmlr.org/papers/v25/22-1367.html

#classifiers #ensembles #en

'An Asymptotic Study of Discriminant and Vote-Averaging Schemes for Randomly-Projected Linear Discriminants', by Lama B. Niyazi, Abla Kammoun, Hayssam Dahrouj, Mohamed-Slim Alouini, Tareq Y. Al-Naffouri.

http://jmlr.org/papers/v25/22-1367.html

#classifiers #ensembles #en

'An Asymptotic Study of Discriminant and Vote-Averaging Schemes for Randomly-Projected Linear Discriminants', by Lama B. Niyazi, Abla Kammoun, Hayssam Dahrouj, Mohamed-Slim Alouini, Tareq Y. Al-Naffouri.

http://jmlr.org/papers/v25/22-1367.html

#classifiers #ensembles #en

'Non-splitting Neyman-Pearson Classifiers', by Jingming Wang, Lucy Xia, Zhigang Bao, Xin Tong.

http://jmlr.org/papers/v25/22-0795.html

#classifiers #classifier #classification

'Generalization and Stability of Interpolating Neural Networks with Minimal Width', by Hossein Taheri, Christos Thrampoulidis.

http://jmlr.org/papers/v25/23-0422.html

#classifiers #generalization #minimization

'Generalization and Stability of Interpolating Neural Networks with Minimal Width', by Hossein Taheri, Christos Thrampoulidis.

http://jmlr.org/papers/v25/23-0422.html

#classifiers #generalization #minimization

'Generalization and Stability of Interpolating Neural Networks with Minimal Width', by Hossein Taheri, Christos Thrampoulidis.

http://jmlr.org/papers/v25/23-0422.html

#classifiers #generalization #minimization

'Generalization and Stability of Interpolating Neural Networks with Minimal Width', by Hossein Taheri, Christos Thrampoulidis.

http://jmlr.org/papers/v25/23-0422.html

#classifiers #generalization #minimization

'Generalization and Stability of Interpolating Neural Networks with Minimal Width', by Hossein Taheri, Christos Thrampoulidis.

http://jmlr.org/papers/v25/23-0422.html

#classifiers #generalization #minimization

'Fairness guarantees in multi-class classification with demographic parity', by Christophe Denis, Romuald Elie, Mohamed Hebiri, François Hu.

http://jmlr.org/papers/v25/23-0322.html

#fairness #classifiers #classification

'Margin-Based Active Learning of Classifiers', by Marco Bressan, Nicolò Cesa-Bianchi, Silvio Lattanzi, Andrea Paudice.

http://jmlr.org/papers/v25/22-1127.html

#classifiers #classes #algorithms

'Classification with Deep Neural Networks and Logistic Loss', by Zihan Zhang, Lei Shi, Ding-Xuan Zhou.

http://jmlr.org/papers/v25/22-0049.html

#classifiers #deepen #classification

'Multi-class Probabilistic Bounds for Majority Vote Classifiers with Partially Labeled Data', by Vasilii Feofanov, Emilie Devijver, Massih-Reza Amini.

http://jmlr.org/papers/v25/23-0121.html

#classifiers #classifier #labeling

'A Multilabel Classification Framework for Approximate Nearest Neighbor Search', by Ville Hyvönen, Elias Jääsaari, Teemu Roos.

http://jmlr.org/papers/v25/23-0286.html

#classification #classifiers #classifier

'Random Feature Amplification: Feature Learning and Generalization in Neural Networks', by Spencer Frei, Niladri S. Chatterji, Peter L. Bartlett.

http://jmlr.org/papers/v24/22-1132.html

#classifiers #neurons #relu

'Random Feature Amplification: Feature Learning and Generalization in Neural Networks', by Spencer Frei, Niladri S. Chatterji, Peter L. Bartlett.

http://jmlr.org/papers/v24/22-1132.html

#classifiers #neurons #relu

'Random Feature Amplification: Feature Learning and Generalization in Neural Networks', by Spencer Frei, Niladri S. Chatterji, Peter L. Bartlett.

http://jmlr.org/papers/v24/22-1132.html

#classifiers #neurons #relu

'Random Feature Amplification: Feature Learning and Generalization in Neural Networks', by Spencer Frei, Niladri S. Chatterji, Peter L. Bartlett.

http://jmlr.org/papers/v24/22-1132.html

#classifiers #neurons #relu

'Random Feature Amplification: Feature Learning and Generalization in Neural Networks', by Spencer Frei, Niladri S. Chatterji, Peter L. Bartlett.

http://jmlr.org/papers/v24/22-1132.html

#classifiers #neurons #relu

'Lifted Bregman Training of Neural Networks', by Xiaoyu Wang, Martin Benning.

http://jmlr.org/papers/v24/22-0934.html

#autoencoders #classifiers #denoising

'Statistical Comparisons of Classifiers by Generalized Stochastic Dominance', by Christoph Jansen, Malte Nalenz, Georg Schollmeyer, Thomas Augustin.

http://jmlr.org/papers/v24/22-0902.html

#classifiers #comparisons #randomization

'Statistical Comparisons of Classifiers by Generalized Stochastic Dominance', by Christoph Jansen, Malte Nalenz, Georg Schollmeyer, Thomas Augustin.

http://jmlr.org/papers/v24/22-0902.html

#classifiers #comparisons #randomization

'Statistical Comparisons of Classifiers by Generalized Stochastic Dominance', by Christoph Jansen, Malte Nalenz, Georg Schollmeyer, Thomas Augustin.

http://jmlr.org/papers/v24/22-0902.html

#classifiers #comparisons #randomization

'Statistical Comparisons of Classifiers by Generalized Stochastic Dominance', by Christoph Jansen, Malte Nalenz, Georg Schollmeyer, Thomas Augustin.

http://jmlr.org/papers/v24/22-0902.html

#classifiers #comparisons #randomization

'Interpretable and Fair Boolean Rule Sets via Column Generation', by Connor Lawless, Sanjeeb Dash, Oktay Gunluk, Dennis Wei.

http://jmlr.org/papers/v24/22-0880.html

#boolean #classifiers #fairness

'Random Forests for Change Point Detection', by Malte Londschien, Peter Bühlmann, Solt Kovács.

http://jmlr.org/papers/v24/22-0512.html

#changeforest #classifier #classifiers

'Minimax Risk Classifiers with 0-1 Loss', by Santiago Mazuelas, Mauricio Romero, Peter Grunwald.

http://jmlr.org/papers/v24/22-0339.html

#classifiers #classification #supervised

'PAC-learning for Strategic Classification', by Ravi Sundaram, Anil Vullikanti, Haifeng Xu, Fan Yao.

http://jmlr.org/papers/v24/21-1250.html

#adversarial #classifiers #learnability

#python
#AI #IoT #Monitoring of #smart #building
A Comparison of Top 14 Supervised #ML #algorithm for #Room #Occupancy IoT Monitoring

The integration of occupancy detection IoT sensors with smart building ML management systems provides a foundation for smarter and more efficient decisions about space allocation in the workplace.

Based upon the overall model performance and previous studies, we have selected 14 #scikitlearn #classifiers

#explore
https://wp.me/pdMwZd-6xy

Vulnerability-Aware Instance Reweighting For Adversarial Training

Olukorede Fakorede, Ashutosh Kumar Nirala, Modeste Atsague, Jin Tian

Action editor: Qibin Zhao.

https://openreview.net/forum?id=kdPcLdJbt1

#adversarial #classifiers #robustness

'From Classification Accuracy to Proper Scoring Rules: Elicitability of Probabilistic Top List Predictions', by Johannes Resin.

http://jmlr.org/papers/v24/23-0106.html

#classifiers #classification #prediction

New #SurveyCertification:

On Averaging ROC Curves

Jack Hogan, Niall M. Adams

https://openreview.net/forum?id=FByH3qL87G

#classifiers #classifier #roc

On Averaging ROC Curves

Jack Hogan, Niall M. Adams

Action editor: Hsuan-Tien Lin.

https://openreview.net/forum?id=FByH3qL87G

#classifiers #classifier #roc

Finding Competence Regions in Domain Generalization

Jens Müller, Stefan T. Radev, Robert Schmier, Felix Draxler, Carsten Rother, Ullrich Koethe

Action editor: Hanwang Zhang.

https://openreview.net/forum?id=TSy0vuwQFN

#classifiers #accuracy #classifier

'Generalization error bounds for multiclass sparse linear classifiers', by Tomer Levy, Felix Abramovich.

http://jmlr.org/papers/v24/22-0367.html

#classifiers #multiclass #misclassification

Assuming Locally Equal Calibration Errors for Non-Parametric Multiclass Calibration

Kaspar Valk, Meelis Kull

Action editor: Aditya Menon.

https://openreview.net/forum?id=na5sHG69rI

#classifiers #classifier #calibration