Perceptron for Large Margin Classification
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- University of California San Diego,Shannon Laboratory,AT&T
- Organisation type
- Academia,Industry
- Country
- United States of America
- Published
- 1 December 1999
- Authors
- Yoav Freund & Robert E. Schapire
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Character recognition (OCR)
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Training data
- 60,000 tokens
"The dataset consists of 60,000 training examples and 10,000 test examples."
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Citations
- 1,731
Sources
Where this record came from and when it was last checked.
- Reference
- Large Margin Classification Using the Perceptron Algorithm
- Last updated
- 28 November 2025
What the numbers mean
What this model is
Perceptron for Large Margin Classification was published by University of California San Diego,Shannon Laboratory,AT&T, in the country recorded as United States of America, during December 1999. The category the publisher falls under is academia,Industry.
It works in the domain of Vision, and is recorded as performing the task of character recognition (OCR).
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
What went into building it
The training set ran to roughly 60,000 tokens of text.
Answers
Perceptron for Large Margin Classification — common questions
Perceptron for Large Margin Classification— who created it?
It was published by University of California San Diego,Shannon Laboratory,AT&T, based in United States of America, an organisation categorised as academia,Industry.
Perceptron for Large Margin Classification— when was it released?
It was published in December 1999. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Perceptron for Large Margin Classification— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of character recognition (OCR). A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
Perceptron for Large Margin Classification— what GPU do I need to run it?
None. This is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
Perceptron for Large Margin Classification— is it open source?
The licensing was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
Perceptron for Large Margin Classification— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.