A00-1022 ICC-MAIL database for use in future learning steps . Other features of the
A00-1022 to the document categories The learning phase defines thresholds for
A00-1020 . To be able to develop such a learning approach , we must first develop
A00-1022 preprocessing used . During the learning phase , each document is pre
A00-1002 see Haji , Hladka ( 1998 ) . The learning is based on a manually tagged
A00-1022 accuracy after repeating the off-line learning step . If a new category is introduced
A00-1022 different linguistic preprocessing and learning algorithms and provide some interpretations
A00-1022 documents as vectors during the learning phase . In the categorization
A00-1022 with statistics-based machine learning techniques ( SML ) is called
A00-1022 application , we tried out the Learning Vector Quantization ( LVQ ) (
A00-1022 shallow text processing and machine learning techniques . It is implemented
A00-1022 to a certain class during the learning phase , e.g. decision trees ,
A00-1022 kind of " non-symbolic " eager learning algo - 1 60 rithm . The neural
A00-1022 Sommerfield , 1996 ) . Symbolic Eager Learning : This type of learners constructs
A00-1012 of extra features to a machine learning algorithm then it is possible
A00-1022 construct a representation during the learning phase , namely a hyper plane
A00-1022 workflow of the system consists of a learning step carried out off-line ( the
A00-1022 kind of preprocessing and which learning algorithm is most appropriate
A00-1020 as knowledge seeds for machine learning techniques that operate on large
A00-1020 as an ideal framework for text learning tasks that have knowledge seeds
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