Changes between Version 4 and Version 5 of tutorial/ProbabilisticLearningModels

Timestamp:

08/14/09 09:40:11 (15 years ago)

Author:

horak

Comment:

--

tutorial/ProbabilisticLearningModels

@@ -9 +9 @@
 == Introduction ==
 Classification in general is the problem of deciding for a given input to which class it belongs. Usually classification can be subdivided into a learning phase (aka training phase) and a classification phase (aka test phase). (TODO: offline, online, reinforcement, ... learning)
+
+=== Basics ===
+
+There are several basics to concern and understand before diving into probabilistic learning models.
+
+==== Example / Instance ====
+Examples or also called instances are the basic entities in this field.
+They occur as training examples, as validation or test examples, and finally as real data.
+
+{{{
+E.g., In a document classification scenario, examples are documents. 
+Already classified documents are used for training or evaluation purpose.
+}}}
+
+==== Feature ====
+A feature is a descriptive property of an example. Features are processible by machines. 
+{{{
+E.g., In a document classification scenario, features might be the words of a document.
+In consequence, single features might describe multiple examples (here documents)
+}}}
+==== Feature Extraction ====
+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.
+}}}
+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. 
+}}}
+
+==== Feature Selection ====
+Each feature for each example has be processed by model trainers or executors.
+There are several reasons for selecting just subsets of existing features.
+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.
+}}}
+Second, just a small set of features might be enough for classifiying examples successfully. Adding more just decreases  performance. 
 
 === Relational Classification ===
@@ -42 +80 @@
 
 
+
+
 == Appendix ==
 === Mathematical Foundations ===