Boosting
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
- Bell Laboratories
- Organisation type
- Industry
- Country
- United States of America
- Published
- 30 November 1992
- Authors
- H. Drucker, R. Schapire, Patrice Y. Simard
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Digit recognition
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.
- Parameters
- 2.6K
- Training data
- 29,127 tokens
“The network has 4645 neurons, 2578 different weights, and 98442 connections.“
“divided into 9709 training examples and 2007 validation samples.”
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- Historical significance
- Record confidence
- Likely
Sources
Where this record came from and when it was last checked.
- Reference
- Improving Performance in Neural Networks Using a Boosting Algorithm
- Last updated
- 28 November 2025
What the numbers mean
About this model
Boosting was published by Bell Laboratories, in the country recorded as United States of America, during November 1992. The category the publisher falls under is industry.
It works in the domain of Vision, and is recorded as performing the task of digit recognition.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
What went into building it
It was trained on a corpus of about 29,127 tokens of text.
The reason it appears in this catalogue at all: historical significance.
Answers
Boosting — common questions
Boosting— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of digit recognition. These are the areas it was designed around; they describe intent rather than a hard boundary.
Boosting— 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.
Boosting— 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.
Boosting— how many parameters does it have?
It has a parameter count of 2.6K. “The network has 4645 neurons, 2578 different weights, and 98442 connections.“. That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
Boosting— who created it?
It was published by Bell Laboratories, based in United States of America, an organisation categorised as industry.
Boosting— when was it released?
It was published in November 1992. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
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.