Lmst

#thingsitried #facialrecognition #opencv #HaarCascade #classifier

https://thingsitried.com/2025/04/06/facial-recognition-with-webcam/

'Consistent Multiclass Algorithms for Complex Metrics and Constraints', by Harikrishna Narasimhan et al.

http://jmlr.org/papers/v25/22-1137.html

#multiclass #classifier #classification

'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

'Regimes of No Gain in Multi-class Active Learning', by Gan Yuan, Yunfan Zhao, Samory Kpotufe.

http://jmlr.org/papers/v25/23-0234.html

#classifier #classification #classes

https://huggingface.co/datasets/mofosyne/stupidfilter

Blast from the past! Here's a dataset from the StupidFilter project around 2007 by Gabriel Ortiz and Paul Starr.

It was an effort to collate all the messages in social media at the time an rank it by stupidity to try and train a filter to filter out stupidity.

However I think the project somewhat fell though as it wasn't able to fully detect all the stupidity in youtube and other site with the technology at the time...

#ai #stupidfilter #LLMs #classifier

How would you go about creating a filter that blocks posts about things that people hate?

I've thought I could build a text classifier, but it could be hard to train since I'd need to guess whether or not the author hates the thing they are posting about.

I wouldn't want it to become a filter for all current events news, but I suspect that's what it would become.

#fediverse #mastodon #machineLearning #tfidf #classification #socialMedia #classifier #textAnalysis #programming #tech #technology

'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

'On the Learnability of Out-of-distribution Detection', by Zhen Fang, Yixuan Li, Feng Liu, Bo Han, Jie Lu.

http://jmlr.org/papers/v25/23-1257.html

#learnability #classifier #detection

'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

Thanks !!!! :blobaww:

https://www.kaggle.com/code/sc0v1n0/k-means-classifier-recommend-movies

#kaggle #notebook #jupyternotebook #learn #learning #kmeans #classifier #sklearn #pandas #python

Except for naming the shapes I figure something like following might work?

- Render each Unicode script in several fonts.

- Training per-script classifiers (OCRs) on those.

- Run the per-script classifiers on each others training data to identify homographs.

There's a tricky bits, like creating non-confusable out-of-script scribbles to include in training data, etc.

Still... this doesn't seem like all that impossible?

What am I missing?

#unicode #homography #ml #classifier

'On Truthing Issues in Supervised Classification', by Jonathan K. Su.

http://jmlr.org/papers/v25/19-301.html

#classification #classifier #supervised

Делаем intent classifier для службы поддержки без доменного датасета на русском

В этой статье я продемонстрирую, как без собственного датасета сделать классификатор намерений пользователя для службы поддержки в сфере e-commerce. И более того, я расскажу, как у меня получилось сделать классификатор для русского языка без датасета на русском языке. Меня зовут Елизавета Колмакова, я Data Scientist в компании, которая разрабатывает айти-решения для крупного ритейла.

https://habr.com/ru/articles/792542/

#intent_recognition #intent #encoder #служба_поддержки #no_dataset #classifier #telegrambot #nlp #bot

'Set-valued Classification with Out-of-distribution Detection for Many Classes', by Zhou Wang, Xingye Qiao.

http://jmlr.org/papers/v24/23-0712.html

#classifier #classification #classes

'MAUVE Scores for Generative Models: Theory and Practice', by Krishna Pillutla et al.

http://jmlr.org/papers/v24/23-0023.html

#generative #classifier #divergences

'Erratum: Risk Bounds for the Majority Vote: From a PAC-Bayesian Analysis to a Learning Algorithm', by Louis-Philippe Vignault, Audrey Durand, Pascal Germain.

http://jmlr.org/papers/v24/22-0747.html

#bound #classifier #majority