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Nondistributed Representations
Representations that may contain many entries but without significant meaningful separate control over each entry. Many learning algorithms are based on such representations, such as:
- Clustering methods
- K-nearest neighbors algorithms
- Decision trees
- Gaussian mixtures and mixtures of experts
- Kernel machines with a Gaussian (or other similarly local) kernel
- Language or translation models based on n-grams
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References
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Data Science
Related
Rules-Based Systems vs. Classic Machine Learning vs. Representation Learning vs. Deep Learning
Methods of Feature Learning
Distributed Representations
How does Unsupervised Pretraining act as a regularizer?
Disadvantage of Pretraining
When to use greedy unsupervised pretraining
Greedy Layer-Wise Unsupervised Pretraining
Data Augmentation
Structured Prediction
Manual Feature Engineering in Computer Vision
Learn After
Learn After
Related
Rules-Based Systems vs. Classic Machine Learning vs. Representation Learning vs. Deep Learning
Methods of Feature Learning
Distributed Representations
How does Unsupervised Pretraining act as a regularizer?
Disadvantage of Pretraining
When to use greedy unsupervised pretraining
Greedy Layer-Wise Unsupervised Pretraining
Data Augmentation
Structured Prediction
Manual Feature Engineering in Computer Vision