Who Cited It

Solving Multiclass Learning Problems via Error-Correcting Output Codes

1995 · Journal of Artificial Intelligence Research · 2,729 citations · 15 from inside this corpus

Tom Dietterich, Ghulum Bakiri low

Multiclass learning problems involve finding a definitionfor an unknown function f(x) whose range is a discrete setcontaining k > 2 values (i.e., k ``classes''). Thedefinition is acquired by studying collections of training examples ofthe form [x_i, f (x_i)]. Existing approaches tomulticlass learning problems include direct application of multiclassalgorithms such as the decision-tree algorithms C4.5 and CART,application of binary concept learning algorithms to learn individualbinary functions for each of the k classes, and application ofbinary concept learning algorithms with distributed outputrepresentations. This paper compares these three approaches to a newtechnique in which error-correcting codes are employed as adistributed output representation. We show that these outputrepresentations improve the generalization performance of both C4.5and backpropagation on a wide range of multiclass learning tasks. Wealso demonstrate that this approach is robust with respect to changesin the size of the training sample, the assignment of distributedrepresentations to particular classes, and the application ofoverfitting avoidance techniques such as decision-tree pruning.Finally, we show that---like the other methods---the error-correctingcode technique can provide reliable class probability estimates.Taken together, these results demonstrate that error-correcting outputcodes provide a general-purpose method for improving the performanceof inductive learning programs on multiclass problems.

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What this paper cites, inside the corpus

PaperYearCited
Classification and Regression Trees.198621,044
Learning Internal Representations by Error Propagation198516,273
The perceptron: A probabilistic model for information storage and organization in the bra…195811,965
The Strength of Weak Learnability19903,344
Learning Internal Representations by Error Propagation19883,036
Phoneme recognition using time-delay neural networks19892,650
Parallel Networks that Learn to Pronounce English Text19871,557
Connectionist learning procedures19891,466

What cites it, inside the corpus

PaperYearCited
Ensemble Methods in Machine Learning20008,058
Multitask Learning19976,478
Distance Metric Learning for Large Margin Nearest Neighbor Classification20094,256
Ensemble learning: A survey20183,088
Understanding Machine Learning: From Theory To Algorithms20153,081
Ensemble based systems in decision making20062,965
A Short Introduction to Boosting19992,952
Improved boosting algorithms using confidence-rated predictions19982,573
Boosting the margin: a new explanation for the effectiveness of voting methods19982,344
The Boosting Approach to Machine Learning: An Overview20032,023
Improved Boosting Algorithms Using Confidence-rated Predictions19991,966
On the algorithmic implementation of multiclass kernel-based vector machines20021,789
Distance Metric Learning for Large Margin Nearest Neighbor Classification20051,707
Attribute-Based Classification for Zero-Shot Visual Object Categorization20131,674
Neural networks for classification: a survey20001,672

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Machine Learning and AlgorithmsComputer Science
Imbalanced Data Classification TechniquesComputer Science
Machine Learning and Data ClassificationComputer Science

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