Mastodawn

'chicken soup', 44 x 54 cm, acryl on canvas, 2026
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Price range € 900 - € 1.000
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#beeldendekunst #hedendaagseschilderkunst #kunst #hedendaagsekunst #contemporarypainting #contemporaryart #modernpainting #modernart #minimalart #abstractart #figurativeart #styleless #thenetherlands #zeitgenössischekunst #zeitgenössischemalerei #steven_van_meurs #stevenvanmeurs #svm

sayzard Apr 29

Nomad_Sim (@sedonaroxx)

과적합이 아닌 과매개변수화 모델에서 파라미터 수가 증가할수록 더 다양한 방식으로 적합할 수 있어, 훈련에서 발견되지 않은 잠재 구조를 학습할 수 있다는 관점을 설명했다. 로짓 모델과 SVM의 커널 고차원 투영을 예로 들어, 더 큰 모델의 일반화 직관을 논의한 내용이다.

https://x.com/sedonaroxx/status/2049440218634494424

#ml #overparameterization #svm #generalization #theory

Nomad_Sim (@sedonaroxx) on X

@_avichawla We observe this in logit and SVMs (where the kernel projects onto high dims); the intuition is higher parameters help to fit the model in more ways and the model learns underlying latent structure not discovered in training. Happens with over parameterized models. Fascinating

X (formerly Twitter)

steven van meurs Apr 13

’reloaded (and turned halfway)’, 64 x 68 cm, acrylic on canvas, 2026
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Price range € 1.250 - € 1.500
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#beeldendekunst #hedendaagseschilderkunst #kunst #hedendaagsekunst #contemporarypainting #contemporaryart #modernpainting #modernart #minimalart #abstractart #figurativeart #styleless #thenetherlands #zeitgenössischekunst #zeitgenössischemalerei #steven_van_meurs #stevenvanmeurs #svm

steven van meurs Mar 12

’hatching_diamond’, 48 x 56 cm, acrylic on canvas, 2026
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Price range € 1.000 - € 1.250
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#beeldendekunst #hedendaagseschilderkunst #kunst #hedendaagsekunst #contemporarypainting #contemporaryart #modernpainting #modernart #minimalart #abstractart #figurativeart #styleless #thenetherlands #zeitgenössischekunst #zeitgenössischemalerei #steven_van_meurs #stevenvanmeurs #svm

Reel Tubes Mar 12

Un chouette reportage SVM : une novice (Jeanne‑Marie) découvre un EeePC sous GNU/Linux — douceur, surprises et petites astuces pour débuter. À voir si vous aimez la découverte, le libre et la simplicité matérielle ! #EeePC #GNULinux #Linux #OpenSource #Framasoft #SVM #French
https://peertube.giftedmc.com/videos/watch/3a63563b-1f08-4145-9c8c-d7b21aabfe08

EeePC sur GNU/Linux

PeerTube

ansuz Mar 9

Given how vocal I am against the "AI" industry, some of my followers might be surprised to learn that I'm now a co-author on a machine learning paper.

That paper has been submitted to the proceedings of an upcoming conference under their "Responsible AI" track, but it has nothing to do with LLMs or really anything that has recently been pushed by the industry's hype-machine. A pre-print is available on arxiv.org ("Tiny, Hardware-Independent, Compression-based Classification") while its formal review is pending.

Our paper expands on a technique I've been using to classify my emails for more than two years called "NCD-KNN" (Normalized Compression Distance with K-Nearest Neighbours). This method uses commonly available compression utilities like GZIP to estimate the relative "distance" between an input and a set of labeled examples, ultimately categorizing that input according to the labels of the K-nearest examples.

We solved some fundamental problems which could result in negative distances under specific circumstances which we identified, addressed some other theoretical limitations which prevented its broader use, and extended NCD to applications using Support-Vector-Machines (SVMs) for non-linear classification.

My co-authors are not on Fedi, but if any of this interests you then feel free to Ask Me Anything

#AMA #ML #AI #SVM #NCD #KNN

Tiny, Hardware-Independent, Compression-based Classification

The recent developments in machine learning have highlighted a conflict between online platforms and their users in terms of privacy. The importance of user privacy and the struggle for power over user data has been intensified as regulators and operators attempt to police online platforms. As users have become increasingly aware of privacy issues, client-side data storage, management, and analysis have become a favoured approach to large-scale centralised machine learning. However, state-of-the-art machine learning methods require vast amounts of labelled user data, making them unsuitable for models that reside client-side and only have access to a single user's data. State-of-the-art methods are also computationally expensive, which degrades the user experience on compute-limited hardware and also reduces battery life. A recent alternative approach has proven remarkably successful in classification tasks across a wide variety of data -- using a compression-based distance measure (called normalised compression distance) to measure the distance between generic objects in classical distance-based machine learning methods. In this work, we demonstrate that the normalised compression distance is actually not a metric; develop it for the wider context of kernel methods to allow modelling of complex data; and present techniques to improve the training time of models that use this distance measure. We demonstrate that the normalised compression distance works as well as and sometimes better than other metrics and kernels -- while requiring only marginally more computational costs and in spite of the lack of formal metric properties. The end results is a simple model with remarkable accuracy even when trained on a very small number of samples allowing for models that are small and effective enough to run entirely on a client device using only user-supplied data.

arXiv.org

steven van meurs Mar 5