SNoW User Guide

Authors:
Andrew J. Carlson;Chad M. Cumby;Jeff L. Rosen;Dan Roth
Affiliations:
-;-;-;-
Venue:
SNoW User Guide
Year:
1999

Citing 0
Cited 13

A Comparison of Face/Non-face Classifiers

AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Question classification using support vector machines

Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Automated categorization in the international patent classification

ACM SIGIR Forum
MPTP -- Motivation, Implementation, First Experiments

Journal of Automated Reasoning
Unsupervised named entity classification models and their ensembles

COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Prospective recruitment of patients with congestive heart failure using an ad-hoc binary classifier

Journal of Biomedical Informatics
MaLARea SG1 - Machine Learner for Automated Reasoning with Semantic Guidance

IJCAR '08 Proceedings of the 4th international joint conference on Automated Reasoning
Improving sequence segmentation learning by predicting trigrams

CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
From symbolic to sub-symbolic information in question classification

Artificial Intelligence Review
MaLeCoP: machine learning connection prover

TABLEAUX'11 Proceedings of the 20th international conference on Automated reasoning with analytic tableaux and related methods
XML-izing mizar: making semantic processing and presentation of MML easy

MKM'05 Proceedings of the 4th international conference on Mathematical Knowledge Management
MaSh: machine learning for sledgehammer

ITP'13 Proceedings of the 4th international conference on Interactive Theorem Proving
Theorem proving in large formal mathematics as an emerging AI field

Automated Reasoning and Mathematics

Quantified Score

Hi-index	0.00

Visualization

Abstract

SNoW is a learning architecture that is specifically tailored for learning in the presence of a very large number of features and can be used as a general purpose multi-class classifier. SNoW has been used successfully in several applications in the natural language and visual processing domains. This document is the user guide for the SNoW learning architecture. It is provided, along with the software, as a research tool for studying learning in these domains.