# Constraint Classification for Multiclass Classification and Ranking

@inproceedings{HarPeled2002ConstraintCF, title={Constraint Classification for Multiclass Classification and Ranking}, author={Sariel Har-Peled and Dan Roth and Dav Zimak}, booktitle={NIPS}, year={2002} }

The constraint classification framework captures many flavors of multiclass classification including winner-take-all multiclass classification, multilabel classification and ranking. We present a meta-algorithm for learning in this framework that learns via a single linear classifier in high dimension. We discuss distribution independent as well as margin-based generalization bounds and present empirical and theoretical evidence showing that constraint classification benefits over existing…

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## References

SHOWING 1-10 OF 27 REFERENCES

### Constraint Classification: A New Approach to Multiclass Classification

- Computer ScienceALT
- 2002

This paper provides the first optimal, distribution independent bounds for many multiclass learning algorithms, including winner-take-all (WTA), and presents a learning algorithm that learns via a single linear classifier in high dimension.

### A Sequential Model for Multi-Class Classification

- Computer ScienceEMNLP
- 2001

A sequential learning model is suggested that utilizes classifiers to sequentially restrict the number of competing classes while maintaining, with high probability, the presence of the true outcome in the candidates set.

### Classification by Pairwise Coupling

- MathematicsNIPS
- 1997

A strategy for polychotomous classification that involves estimating class probabilities for each pair of classes, and then coupling the estimates together is discussed, similar to the Bradley-Terry method for paired comparisons.

### Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers

- Computer ScienceJ. Mach. Learn. Res.
- 2000

A general method for combining the classifiers generated on the binary problems is proposed, and a general empirical multiclass loss bound is proved given the empirical loss of the individual binary learning algorithms.

### Solving Multiclass Learning Problems via Error-Correcting Output Codes

- Computer ScienceJ. Artif. Intell. Res.
- 1995

It is demonstrated that error-correcting output codes provide a general-purpose method for improving the performance of inductive learning programs on multiclass problems.

### Ultraconservative Online Algorithms for Multiclass Problems

- Computer ScienceJ. Mach. Learn. Res.
- 2003

This paper studies online classification algorithms for multiclass problems in the mistake bound model and introduces the notion of ultracon-servativeness, a family of additive ultraconservative algorithms where each algorithm in the family updates its prototypes by finding a feasible solution for a set of linear constraints that depend on the instantaneous similarity-scores.

### Support vector machines for multi-class pattern recognition

- Computer ScienceESANN
- 1999

A formulation of the SVM is proposed that enables a multi-class pattern recognition problem to be solved in a single optimisation and a similar generalization of linear programming machines is proposed.

### On the Learnability and Design of Output Codes for Multiclass Problems

- Computer ScienceMachine Learning
- 2004

This paper discusses for the first time the problem of designing output codes for multiclass problems, and gives a time and space efficient algorithm for solving the quadratic program.

### Mistake-Driven Learning in Text Categorization

- Computer ScienceEMNLP
- 1997

This work studies three mistake-driven learning algorithms for a typical task of this nature -- text categorization and presents an algorithm, a variation of Littlestone's Winnow, which performs significantly better than any other algorithm tested on this task using a similar feature set.

### Characterizations of Learnability for Classes of {0, ..., n}-Valued Functions

- Computer Science, MathematicsJ. Comput. Syst. Sci.
- 1995

A general scheme for extending the VC-dimension to the case n > 1 is presented, which defines a wide variety of notions of dimension in which all these variants of theVC-dimension, previously introduced in the context of learning, appear as special cases.