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research:largescale [2012/12/24 11:52] leonb [Learning with Stochastic Gradient Descent] |
research:largescale [2013/02/25 09:55] leonb [Related] |
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| ===== Approximate Optimization ===== | ===== Approximate Optimization ===== | ||
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| Large-scale machine learning was first approached as an engineering problem. For instance, to leverage a | Large-scale machine learning was first approached as an engineering problem. For instance, to leverage a | ||
| larger training set, we can use a parallel computer to run a known machine learning algorithm | larger training set, we can use a parallel computer to run a known machine learning algorithm | ||
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| takes into account the effect of approximate | takes into account the effect of approximate | ||
| optimization on learning algorithms. | optimization on learning algorithms. | ||
| + | |||
| The analysis shows distinct tradeoffs for the | The analysis shows distinct tradeoffs for the | ||
| case of small-scale and large-scale learning problems. | case of small-scale and large-scale learning problems. | ||
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| complexity of the underlying optimization | complexity of the underlying optimization | ||
| algorithms in non-trivial ways. | algorithms in non-trivial ways. | ||
| + | For instance, [[: | ||
| + | appear to be mediocre optimization algorithms and yet are shown to | ||
| + | [[: | ||
| - | |||
| - | For instance, [[: | ||
| - | appear to be mediocre optimization algorithms | ||
| - | and yet are shown to perform extremely well on large-scale learning problems. | ||
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| * NIPS 2007 tutorial " | * NIPS 2007 tutorial " | ||
| + | ===== Related ===== | ||
| + | |||
| + | * [[: | ||
| ===== Papers ===== | ===== Papers ===== | ||
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| [[: | [[: | ||
| </ | </ | ||
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| - | ===== See also ===== | ||
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| - | * [[stochastic|Learning with Stochastic Gradient Descent]]. | ||
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