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LEADER 00000cam a2200997 a 4500
001 826657834
003 OCoLC
005 20240129213017.0
006 m o d
007 cr cnu---unuuu
008 130204s2011 enka ob 001 0 eng d
010 |z 2010040730
019 857717622|a988429981|a992864308|a1103273886|a1129362547
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020 9781118557693|q(electronic bk.)
020 1118557697|q(electronic bk.)
020 9781118586334|q(electronic bk.)
020 1118586336|q(electronic bk.)
020 9781118586136
020 1118586131
020 1299139914
020 9781299139916
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|dOCLCQ|dOCLCF|dOCLCQ|dDEBBG|dDG1|dMOR|dOCLCA|dLIP|dOCLCQ
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049 INap
082 04 006.3
082 04 006.3|222
099 eBook O’Reilly for Public Libraries
100 1 Albalate, Amparo.
245 10 Semi-supervised and unsupervised machine learning :|bnovel
strategies /|cAmparo Albalate, Wolfgang Minker.|h[O'Reilly
electronic resource]
260 London :|bISTE ;|aHoboken, NJ :|bWiley,|c2011.
300 1 online resource (x, 244 pages) :|billustrations
336 text|btxt|2rdacontent
337 computer|bc|2rdamedia
338 online resource|bcr|2rdacarrier
347 text file
490 1 ISTE
504 Includes bibliographical references and index.
505 00 |gMachine generated contents note:|gpt. 1|tState of the
Art --|gch. 1|tIntroduction --|g1.1.|tOrganization of the
book --|g1.2.|tUtterance corpus --|g1.3.|tDatasets from
the UCI repository --|g1.3.1.|tWine dataset (wine) --
|g1.3.2.|tWisconsin breast cancer dataset (breast) --
|g1.3.3.|tHandwritten digits dataset (Pendig) --|g1.3.4.
|tPima Indians diabetes (diabetes) --|g1.3.5.|tIris
dataset (Iris) --|g1.4.|tMicroarray dataset --|g1.5.
|tSimulated datasets --|g1.5.1.|tMixtures of Gaussians --
|g1.5.2.|tSpatial datasets with non-homogeneous inter-
cluster distance --|gch. 2|tState of the Art in Clustering
and Semi-Supervised Techniques --|g2.1.|tIntroduction --
|g2.2.|tUnsupervised machine learning (clustering) --
|g2.3.|tA brief history of cluster analysis --|g2.4.
|tCluster algorithms --|g2.4.1.|tHierarchical algorithms -
-|g2.4.1.1.|tAgglomerative clustering --|g2.4.1.2.
|tDivisive algorithms --|g2.4.2.|tModel-based clustering -
-|g2.4.2.1.|tThe expectation maximization (EM) algorithm -
-|g2.4.3.|tPartitional competitive models.
505 00 |g2.4.3.1.|tK-means --|g2.4.3.2.|tNeural gas --|g2.4.3.3.
|tPartitioning around Medoids (PAM) --|g2.4.3.4.|tSelf-
organizing maps --|g2.4.4.|tDensity-based clustering --
|g2.4.4.1.|tDirect density reachability --|g2.4.4.2.
|tDensity reachability --|g2.4.4.3.|tDensity connection --
|g2.4.4.4.|tBorder points --|g2.4.4.5.|tNoise points --
|g2.4.4.6.|tDBSCAN algorithm --|g2.4.5.|tGraph-based
clustering --|g2.4.5.1.|tPole-based overlapping clustering
--|g2.4.6.|tAffectation stage --|g2.4.6.1.|tAdvantages and
drawbacks --|g2.5.|tApplications of cluster analysis --
|g2.5.1.|tImage segmentation --|g2.5.2.|tMolecular biology
--|g2.5.2.1.|tBiological considerations --|g2.5.3.
|tInformation retrieval and document clustering --
|g2.5.3.1.|tDocument pre-processing --|g2.5.3.2.|tBoolean
model representation --|g2.5.3.3.|tVector space model --
|g2.5.3.4.|tTerm weighting --|g2.5.3.5.|tProbabilistic
models --|g2.5.4.|tClustering documents in information
retrieval --|g2.5.4.1.|tClustering of presented results --
|g2.5.4.2.|tPost-retrieval document browsing (Scatter-
Gather) --|g2.6.|tEvaluation methods.
505 00 |g2.7.|tInternal cluster evaluation --|g2.7.1.|tEntropy --
|g2.7.2.|tPurity --|g2.7.3.|tNormalized mutual information
--|g2.8.|tExternal cluster validation --|g2.8.1.|tHartigan
--|g2.8.2.|tDavies Bouldin index --|g2.8.3.|tKrzanowski
and Lai index --|g2.8.4.|tSilhouette --|g2.8.5.|tGap
statistic --|g2.9.|tSemi-supervised learning --|g2.9.1.
|tSelf training --|g2.9.2.|tCo-training --|g2.9.3.
|tGenerative models --|g2.10.|tSummary --|gpt. 2
|tApproaches to Semi-Supervised Classification --|gch. 3
|tSemi-Supervised Classification Using Prior Word
Clustering --|g3.1.|tIntroduction --|g3.2.|tDataset --
|g3.3.|tUtterance classification scheme --|g3.3.1.|tPre-
processing --|g3.3.1.1.|tUtterance vector representation -
-|g3.3.2.|tUtterance classification --|g3.4.|tSemi-
supervised approach based on term clustering --|g3.4.1.
|tTerm clustering --|g3.4.2.|tSemantic term dissimilarity
--|g3.4.2.1.|tTerm vector of lexical co-occurrences --
|g3.4.2.2.|tMetric of dissimilarity --|g3.4.3.|tTerm
vector truncation --|g3.4.4.|tTerm clustering --|g3.4.5.
|tFeature extraction and utterance feature vector.
505 00 |g3.4.6.|tEvaluation --|g3.5.|tDisambiguation --|g3.5.1.
|tEvaluation --|g3.6.|tSummary --|gch. 4|tSemi-Supervised
Classification Using Pattern Clustering --|g4.1.
|tIntroduction --|g4.2.|tNew semi-supervised algorithm
using the cluster and label strategy --|g4.2.1.|tBlock
diagram --|g4.2.1.1.|tDataset --|g4.2.1.2.|tClustering --
|g4.2.1.3.|tOptimum cluster labeling --|g4.2.1.4.
|tClassification --|g4.3.|tOptimum cluster labeling --
|g4.3.1.|tProblem definition --|g4.3.2.|tThe Hungarian
algorithm --|g4.3.2.1.|tWeighted complete bipartite graph
--|g4.3.2.2.|tMatching, perfect matching and maximum
weight matching --|g4.3.2.3.|tObjective of Hungarian
method --|g4.3.2.4.|tComplexity considerations --|g4.3.3.
|tGenetic algorithms --|g4.3.3.1.|tReproduction operators
--|g4.3.3.2.|tForming the next generation --|g4.3.3.3.
|tGAs applied to optimum cluster labeling --|g4.3.3.4.
|tComparison of methods --|g4.4.|tSupervised
classification block --|g4.4.1.|tSupport vector machines -
-|g4.4.1.1.|tThe kernel trick for nonlinearly separable
classes --|g4.4.1.2.|tMulti-class classification --
|g4.4.2.|tExample.
505 00 |g4.5.|tDatasets --|g4.5.1.|tMixtures of Gaussians --
|g4.5.2.|tDatasets from the UCI repository --|g4.5.2.1.
|tIris dataset (Iris) --|g4.5.2.2.|tWine dataset (wine) --
|g4.5.2.3.|tWisconsin breast cancer dataset (breast) --
|g4.5.2.4.|tHandwritten digits dataset (Pendig) --
|g4.5.2.5.|tPima Indians diabetes (diabetes) --|g4.5.3.
|tUtterance dataset --|g4.6.|tAn analysis of the bounds
for the cluster and label approaches --|g4.7.|tExtension
through cluster pruning --|g4.7.1.|tDetermination of
silhouette thresholds --|g4.7.2.|tEvaluation of the
cluster pruning approach --|g4.8.|tSimulations and results
--|g4.9.|tSummary --|gpt. 3|tContributions to Unsupervised
Classification -- Algorithms to Detect the Optimal Number
of Clusters --|gch. 5|tDetection of the Number of Clusters
through Non-Parametric Clustering Algorithms --|g5.1.
|tIntroduction --|g5.2.|tNew hierarchical pole-based
clustering algorithm --|g5.2.1.|tPole-based clustering
basis module --|g5.2.2.|tHierarchical pole-based
clustering --|g5.3.|tEvaluation --|g5.3.1.|tCluster
evaluation metrics --|g5.4.|tDatasets.
505 00 |g5.4.1.|tResults --|g5.4.2.|tComplexity considerations
for large databases --|g5.5.|tSummary --|gch. 6|tDetecting
the Number of Clusters through Cluster Validation --|g6.1.
|tIntroduction --|g6.2.|tCluster validation methods --
|g6.2.1.|tDunn index --|g6.2.2.|tHartigan --|g6.2.3.
|tDavies Bouldin index --|g6.2.4.|tKrzanowski and Lai
index --|g6.2.5.|tSilhouette --|g6.2.6.|tHubert's & gamma;
--|g6.2.7.|tGap statistic --|g6.3.|tCombination approach
based on quantiles --|g6.4.|tDatasets --|g6.4.1.|tMixtures
of Gaussians --|g6.4.2.|tCancer DNA-microarray dataset --
|g6.4.3.|tIris dataset --|g6.5.|tResults --|g6.5.1.
|tValidation results of the five Gaussian dataset --
|g6.5.2.|tValidation results of the mixture of seven
Gaussians --|g6.5.3.|tValidation results of the NCI60
dataset --|g6.5.4.|tValidation results of the Iris dataset
--|g6.5.5.|tDiscussion --|g6.6.|tApplication of speech
utterances --|g6.7.|tSummary.
520 "This book provides a detailed and up-to-date overview on
classification and data mining methods. The first part is
focused on supervised classification algorithms and their
applications, including recent research on the combination
of classifiers. The second part deals with unsupervised
data mining and knowledge discovery, with special
attention to text mining. Discovering the underlying
structure on a data set has been a key research topic
associated to unsupervised techniques with multiple
applications and challenges, from web-content mining to
the inference of cancer subtypes in genomic microarray
data. Among those, the book focuses on a new application
for dialog systems which can be thereby made adaptable and
portable to different domains. Clustering evaluation
metrics and new approaches, such as the ensembles of
clustering algorithms, are also described"--|cProvided by
publisher
546 English.
588 0 Print version record.
590 O'Reilly|bO'Reilly Online Learning: Academic/Public
Library Edition
650 0 Data mining.
650 0 Discourse analysis|xStatistical methods.
650 0 Speech processing systems.
650 0 Computational intelligence.
650 2 Data Mining
650 6 Exploration de données (Informatique)
650 6 Traitement automatique de la parole.
650 6 Intelligence informatique.
650 7 Computational intelligence|2fast
650 7 Data mining|2fast
650 7 Discourse analysis|xStatistical methods|2fast
650 7 Speech processing systems|2fast
700 1 Minker, Wolfgang.
776 08 |iPrint version:|aAlbalate, Amparo.|tSemi-supervised and
unsupervised machine learning.|dLondon : ISTE ; Hoboken,
NJ : Wiley, 2011|z9781848212039|w(DLC) 2010040730
|w(OCoLC)700509842
830 0 ISTE.
856 40 |uhttps://ezproxy.naperville-lib.org/login?url=https://
learning.oreilly.com/library/view/~/9781118586136/?ar
|zAvailable on O'Reilly for Public Libraries
938 Askews and Holts Library Services|bASKH|nAH25046162
938 ebrary|bEBRY|nebr10653856
938 EBSCOhost|bEBSC|n529223
938 ProQuest MyiLibrary Digital eBook Collection|bIDEB
|ncis24744256
938 YBP Library Services|bYANK|n9984750
994 92|bJFN