Changes between Version 5 and Version 6 of tutorial/ProbabilisticLearningModels

Timestamp:

08/14/09 09:40:47 (15 years ago)

Author:

horak

Comment:

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tutorial/ProbabilisticLearningModels

@@ -32 +32 @@
 Feature extraction is the task of extracting features from examples.
 {{{
-E.g., In our document classification scenario, a tokenizer that extracts words from text might be used for feature extraction.
+E.g., In our document classification scenario, a tokenizer that extracts words from 
+text might be used for feature extraction.
 }}}
 In more sophisticated scenarios, feature extraction can be hierarchically nested by extracting new features from existing feature lists.
 {{{
-E.g., In our document classification scenario, a word n-gram algorithm extracts n-gram features from extracted word sequences. 
+E.g., In our document classification scenario, a word n-gram algorithm extracts n-gram 
+features from extracted word sequences. 
 }}}
 
@@ -44 +46 @@
 First, not all features are useful for separating different classes. In details, there is no statistically significant dependency between class and feature occurance.
 {{{
-E.g., In our document classification scenario, stop words or high frequent words are not useful for separating e.g., spam mails from ham mails.
+E.g., In our document classification scenario, stop words or high frequent words are 
+not useful for separating e.g., spam mails from ham mails.
 }}}
 Second, just a small set of features might be enough for classifiying examples successfully. Adding more just decreases  performance.