# Differences

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courses:rg:2013:convolution-kernels [2013/03/11 18:42] dusek |
courses:rg:2013:convolution-kernels [2013/03/12 11:27] (current) popel <latex>x</latex> was not rendered |
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* They are able to " | * They are able to " | ||

* Examples: Naive Bayes, Mixtures of Gaussians, HMM, Bayesian Networks, Markov Random Fields | * Examples: Naive Bayes, Mixtures of Gaussians, HMM, Bayesian Networks, Markov Random Fields | ||

- | * **Discriminative models** do everything in one-step -- they learn the posterior < | + | * **Discriminative models** do everything in one-step -- they learn the posterior < |

* They are simpler and can use many more features, but are prone to missing inputs. | * They are simpler and can use many more features, but are prone to missing inputs. | ||

- | * Examples: SVM, Logistic Regression, Neuron. sítě, k-NN, Conditional Random Fields | + | * Examples: SVM, Logistic Regression, Neural network, k-NN, Conditional Random Fields |

- Each CFG rule generates just one level of the derivation tree. Therefore, using " | - Each CFG rule generates just one level of the derivation tree. Therefore, using " | ||

* '' | * '' | ||

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- < | - < | ||

- < | - < | ||

- | - < | + | - < |

+ | - Convolution is defined like this: < | ||

+ | - There is a (tiny) error in the last formula of Section 3. You cannot actually multiply tree parses, so it should read: < | ||

+ | | ||

+ | ==== Report ==== | ||

+ | | ||

+ | We discussed the answers to the questions most of the time. Other issues raised in the discussion were: | ||

+ | | ||

+ | * **Usability** -- the approach is only usable for // | ||

+ | * **Scalability** -- they only use 800 sentences and 20 candidates per sentence for training. We believe that for large data (milions of examples) this will become too complex. | ||

+ | * **Evaluation** -- it looks as if they used a non-standard evaluation metric to get " |