Abstract:
We present a new supervised learning procedure for ensemble machines, in which outputs of predictors, trained on different distributions, are combined by a dynamic classi...Show MoreMetadata
Abstract:
We present a new supervised learning procedure for ensemble machines, in which outputs of predictors, trained on different distributions, are combined by a dynamic classifier combination model. This procedure may be viewed as either a version of mixture of experts (Jacobs, Jordan, Nowlan, & Hinton, 1991), applied to classification, or a variant of the boosting algorithm (Schapire, 1990). As a variant of the mixture of experts, it can be made appropriate for general classification and regression problems by initializing the partition of the data set to different experts in a boostlike manner. If viewed as a variant of the boosting algorithm, its main gain is the use of a dynamic combination model for the outputs of the networks. Results are demonstrated on a synthetic example and a digit recognition task from the NIST database and compared with classical ensemble approaches.
Published in: Neural Computation ( Volume: 11, Issue: 2, 15 February 1999)
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- Index Terms
- Mixture Of Experts ,
- Ensemble Learning Scheme ,
- Dynamic Model ,
- Recognition Task ,
- Regression Problem ,
- Learning Procedure ,
- Data Partitioning ,
- Optical Character Recognition ,
- Synthetic Examples ,
- NIST Database ,
- Training Set ,
- Error Rate ,
- Classification Performance ,
- Learning Task ,
- Highest Yield ,
- Reliability Coefficient ,
- Average Yield ,
- Multilayer Perceptron ,
- Feed-forward Network ,
- Confidence Score ,
- Measure Of Confidence ,
- Gating Function ,
- Parallel Machines ,
- Classification Output ,
- Ensemble Performance ,
- Base Learners ,
- Committee Members ,
- Multilevel Approach ,
- Output Vector ,
- Simple Average
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- Index Terms
- Mixture Of Experts ,
- Ensemble Learning Scheme ,
- Dynamic Model ,
- Recognition Task ,
- Regression Problem ,
- Learning Procedure ,
- Data Partitioning ,
- Optical Character Recognition ,
- Synthetic Examples ,
- NIST Database ,
- Training Set ,
- Error Rate ,
- Classification Performance ,
- Learning Task ,
- Highest Yield ,
- Reliability Coefficient ,
- Average Yield ,
- Multilayer Perceptron ,
- Feed-forward Network ,
- Confidence Score ,
- Measure Of Confidence ,
- Gating Function ,
- Parallel Machines ,
- Classification Output ,
- Ensemble Performance ,
- Base Learners ,
- Committee Members ,
- Multilevel Approach ,
- Output Vector ,
- Simple Average