Search results for "SEKIA"
Koichi SATO (SEKIA)
Lingua::TFIDF - Language-independent TF-IDF calculator.
Quoting Wikipedia <http://en.wikipedia.org/wiki/Tf%E2%80%93idf>: tf–idf, short for term frequency–inverse document frequency, is a numerical statistic that is intended to reflect how important a word is to a document in a collection or corpus. It is ...
SEKIA/Lingua-TFIDF-0.01 - 11 May 2014 17:25:37 UTC - Search in distribution- Lingua::TFIDF::Types
- Lingua::TFIDF::WordSegmenter::SplitBySpace - Simple word segmenter suitable for most european languages
- Lingua::TFIDF::WordSegmenter::JA::MeCab - Word segmenter for Japanese documents
- 3 more results from Lingua-TFIDF »
Algorithm::LibLinear - A Perl binding for LIBLINEAR, a library for classification/regression using linear SVM and logistic regression.
Algorithm::LibLinear is an XS module that provides features of LIBLINEAR, a fast C library for classification and regression. Current version is based on LIBLINEAR 2.47, released on July 9, 2023....
SEKIA/Algorithm-LibLinear-0.26 - 23 Jul 2023 20:08:17 UTC - Search in distribution
Algorithm::AdaBoost - AdaBoost learning algorithm
AdaBoost is a machine learning algorithm proposed by Freund and Schapire. Using an arbitrary binary classification algorithm, The algorithm can construct a more accurate classifier (i.e. it is a meta-algorithm)....
SEKIA/Algorithm-AdaBoost-0.01 - 20 Oct 2012 08:54:18 UTC - Search in distribution
Algorithm::KernelKMeans - Weighted kernel k-means clusterer
"Algorithm::KernelKMeans" provides weighted kernel k-means vector clusterer. Note that this is a very early release. All APIs may be changed incompatibly. IMPLEMENTATION This class is just a placeholder. Implementation code is in other class and this...
SEKIA/Algorithm-KernelKMeans-0.03 - 10 Nov 2010 15:16:15 UTC - Search in distribution
Algorithm::LossyCount - Memory-efficient approximate frequency count.
Lossy-Counting is a approximate frequency counting algorithm proposed by Manku and Motwani in 2002 (refer "SEE ALSO" section below.) The main advantage of the algorithm is memory efficiency. You can get approximate count of appearance of items with v...
SEKIA/Algorithm-LossyCount-0.03 - 18 Mar 2014 03:35:45 UTC - Search in distribution
Search::Fulltext::Tokenizer::Ngram - Character n-gram tokenizer for Search::Fulltext
This module provides character N-gram tokenizers for Search::Fulltext. By default {1,2,3}-gram tokenzers are available....
SEKIA/Search-Fulltext-Tokenizer-Ngram-0.01 - 31 Dec 2013 18:00:45 UTC - Search in distribution