Mathematical foundations of big data analytics:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Berlin Springer Gabler
[2021]
|
| Schlagworte: | |
| Online-Zugang: | Inhaltstext http://www.springer.com/ Inhaltsverzeichnis |
| Beschreibung: | xi, 273 Seiten Illustrationen |
| ISBN: | 9783662625200 3662625202 |
Internformat
MARC
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| 245 | 1 | 0 | |a Mathematical foundations of big data analytics |c Vladimir Shikhman, David Müller |
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| 300 | |a xi, 273 Seiten |b Illustrationen | ||
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| 653 | |a PageRank | ||
| 653 | |a Recommendation Systems | ||
| 653 | |a Neural Networks | ||
| 653 | |a Classification | ||
| 653 | |a Mathematical Models for Big Data Analytics | ||
| 653 | |a Economic Applications of Big Data Analytics | ||
| 653 | |a Clustering | ||
| 653 | |a Analysis of Big Data | ||
| 653 | |a Interdisciplinary Applications of Big Data Analytics | ||
| 653 | |a Online Learning | ||
| 653 | |a Decision Trees | ||
| 653 | |a Sparse Recovery | ||
| 653 | |a Linear Regression | ||
| 653 | |a Big Data | ||
| 653 | |a Statistics, general | ||
| 653 | |a Data Analysis and Big Data | ||
| 653 | |a Economics and Finance | ||
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Datensatz im Suchindex
| _version_ | 1817968183832739840 |
|---|---|
| adam_text |
CONTENTS
1
RANKING
.
1
1.1
MOTIVATION:
GOOGLE
PROBLEM
.
1
1.2
RESULTS
.
4
1.2.1
PERRON-FROBENIUS
THEOREM
.
4
1.2.2
PAGERANK
.
9
1.3
CASE
STUDY:
BRAND
LOYALTY
.
15
1.4
EXERCISES
.
18
2
ONLINE
LEARNING
.
21
2.1
MOTIVATION:
PORTFOLIO
SELECTION
.
22
2.2
RESULTS
.
25
2.2.1
ONLINE
MIRROR
DESCENT
.
25
2.2.2
ENTROPIC
SETUP
.
32
2.3
CASE
STUDY:
EXPERT
ADVICE
.
36
2.4
EXERCISES
.
38
3
RECOMMENDATION
SYSTEMS
.
41
3.1
MOTIVATION:
NETFLIX
PRIZE
.
41
3.2
RESULTS
.
43
3.2.1
NEIGHBORHOOD-BASED
APPROACH
.
43
3.2.2
MODEL-BASED
APPROACH
.
45
3.3
CASE
STUDY:
LATENT
SEMANTIC
ANALYSIS
.
58
3.4
EXERCISES
.
60
4
CLASSIFICATION
.
63
4.1
MOTIVATION:
CREDIT
INVESTIGATION
.
63
4.2
RESULTS
.
65
4.2.1
FISHER
'
S
DISCRIMINANT
RULE
.
65
4.2.2
SUPPORT-VECTOR
MACHINE
.
73
4.3
CASE
STUDY:
QUALITY
CONTROL
.
81
4.4
EXERCISES
.
83
X
CONTENTS
5
CLUSTERING
.
87
5.1
MOTIVATION:
DNA
SEQUENCING
.
87
5.2
RESULTS
.
89
5.2.1
FC-MEANS
.
89
5.2.2
SPECTRAL
CLUSTERING
.
93
5.3
CASE
STUDY:
TOPIC
EXTRACTION
.
100
5.4
EXERCISES
.
103
6
LINEAR
REGRESSION
.
107
6.1
MOTIVATION:
ECONOMETRIC
ANALYSIS
.
108
6.2
RESULTS
.
110
6.2.1
ORDINARY
LEAST
SQUARES
.
110
6.2.2
RIDGE
REGRESSION
.
118
6.3
CASE
STUDY:
CAPITAL
ASSET
PRICING
.
124
6.4
EXERCISES
.
126
7
SPARSE
RECOVERY
.
131
7.1
MOTIVATION:
VARIABLE
SELECTION
.
131
7.2
RESULTS
.
134
7.2.1
LASSO
REGRESSION
.
134
7.2.2
ITERATIVE
SHRINKAGE-THRESHOLDING
ALGORITHM
.
139
7.3
CASE
STUDY:
COMPRESSED
SENSING
.
144
7.4
EXERCISES
.
147
8
NEURAL
NETWORKS
.
149
8.1
MOTIVATION:
NERVE
CELLS
.
150
8.2
RESULTS
.
152
8.2.1
LOGISTIC
REGRESSION
.
152
8.2.2
PERCEPTRON
.
158
8.3
CASE
STUDY:
SPAM
FILTERING
.
165
8.4
EXERCISES
.
167
9
DECISION
TREES
.
171
9.1
MOTIVATION:
TITANIC
SURVIVAL
.
171
9.2
RESULTS
.
175
9.2.1
NP-COMPLETENESS
.
175
9.2.2
TOP-DOWN
AND
BOTTOM-UP
HEURISTICS
.
181
9.3
CASE
STUDY:
CHESS
ENGINE
.
185
9.4
EXERCISES
.
189
10
SOLUTIONS
.
193
10.1
RANKING
.
193
10.2
ONLINE
LEARNING
.
200
10.3
RECOMMENDATION
SYSTEMS
.
206
10.4
CLASSIFICATION
.
215
CONTENTS
XI
10.5
CLUSTERING
.
222
10.6
LINEAR
REGRESSION
.
232
10.7
SPARSITY
.
239
10.8
NEURAL
NETWORKS
.
245
10.9
DECISION
TREES
.
252
BIBLIOGRAPHY
.
265
INDEX
.
269 |
| adam_txt |
CONTENTS
1
RANKING
.
1
1.1
MOTIVATION:
GOOGLE
PROBLEM
.
1
1.2
RESULTS
.
4
1.2.1
PERRON-FROBENIUS
THEOREM
.
4
1.2.2
PAGERANK
.
9
1.3
CASE
STUDY:
BRAND
LOYALTY
.
15
1.4
EXERCISES
.
18
2
ONLINE
LEARNING
.
21
2.1
MOTIVATION:
PORTFOLIO
SELECTION
.
22
2.2
RESULTS
.
25
2.2.1
ONLINE
MIRROR
DESCENT
.
25
2.2.2
ENTROPIC
SETUP
.
32
2.3
CASE
STUDY:
EXPERT
ADVICE
.
36
2.4
EXERCISES
.
38
3
RECOMMENDATION
SYSTEMS
.
41
3.1
MOTIVATION:
NETFLIX
PRIZE
.
41
3.2
RESULTS
.
43
3.2.1
NEIGHBORHOOD-BASED
APPROACH
.
43
3.2.2
MODEL-BASED
APPROACH
.
45
3.3
CASE
STUDY:
LATENT
SEMANTIC
ANALYSIS
.
58
3.4
EXERCISES
.
60
4
CLASSIFICATION
.
63
4.1
MOTIVATION:
CREDIT
INVESTIGATION
.
63
4.2
RESULTS
.
65
4.2.1
FISHER
'
S
DISCRIMINANT
RULE
.
65
4.2.2
SUPPORT-VECTOR
MACHINE
.
73
4.3
CASE
STUDY:
QUALITY
CONTROL
.
81
4.4
EXERCISES
.
83
X
CONTENTS
5
CLUSTERING
.
87
5.1
MOTIVATION:
DNA
SEQUENCING
.
87
5.2
RESULTS
.
89
5.2.1
FC-MEANS
.
89
5.2.2
SPECTRAL
CLUSTERING
.
93
5.3
CASE
STUDY:
TOPIC
EXTRACTION
.
100
5.4
EXERCISES
.
103
6
LINEAR
REGRESSION
.
107
6.1
MOTIVATION:
ECONOMETRIC
ANALYSIS
.
108
6.2
RESULTS
.
110
6.2.1
ORDINARY
LEAST
SQUARES
.
110
6.2.2
RIDGE
REGRESSION
.
118
6.3
CASE
STUDY:
CAPITAL
ASSET
PRICING
.
124
6.4
EXERCISES
.
126
7
SPARSE
RECOVERY
.
131
7.1
MOTIVATION:
VARIABLE
SELECTION
.
131
7.2
RESULTS
.
134
7.2.1
LASSO
REGRESSION
.
134
7.2.2
ITERATIVE
SHRINKAGE-THRESHOLDING
ALGORITHM
.
139
7.3
CASE
STUDY:
COMPRESSED
SENSING
.
144
7.4
EXERCISES
.
147
8
NEURAL
NETWORKS
.
149
8.1
MOTIVATION:
NERVE
CELLS
.
150
8.2
RESULTS
.
152
8.2.1
LOGISTIC
REGRESSION
.
152
8.2.2
PERCEPTRON
.
158
8.3
CASE
STUDY:
SPAM
FILTERING
.
165
8.4
EXERCISES
.
167
9
DECISION
TREES
.
171
9.1
MOTIVATION:
TITANIC
SURVIVAL
.
171
9.2
RESULTS
.
175
9.2.1
NP-COMPLETENESS
.
175
9.2.2
TOP-DOWN
AND
BOTTOM-UP
HEURISTICS
.
181
9.3
CASE
STUDY:
CHESS
ENGINE
.
185
9.4
EXERCISES
.
189
10
SOLUTIONS
.
193
10.1
RANKING
.
193
10.2
ONLINE
LEARNING
.
200
10.3
RECOMMENDATION
SYSTEMS
.
206
10.4
CLASSIFICATION
.
215
CONTENTS
XI
10.5
CLUSTERING
.
222
10.6
LINEAR
REGRESSION
.
232
10.7
SPARSITY
.
239
10.8
NEURAL
NETWORKS
.
245
10.9
DECISION
TREES
.
252
BIBLIOGRAPHY
.
265
INDEX
.
269 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Shikhman, Vladimir 1981- Müller, David |
| author_GND | (DE-588)143928341 |
| author_facet | Shikhman, Vladimir 1981- Müller, David |
| author_role | aut aut |
| author_sort | Shikhman, Vladimir 1981- |
| author_variant | v s vs d m dm |
| building | Verbundindex |
| bvnumber | BV047259186 |
| classification_rvk | QH 234 |
| ctrlnum | (OCoLC)1253630260 (DE-599)DNB1218092858 |
| discipline | Informatik Wirtschaftswissenschaften |
| discipline_str_mv | Informatik Wirtschaftswissenschaften |
| format | Book |
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| genre | (DE-588)4123623-3 Lehrbuch gnd-content |
| genre_facet | Lehrbuch |
| id | DE-604.BV047259186 |
| illustrated | Illustrated |
| index_date | 2024-07-03T17:10:39Z |
| indexdate | 2024-12-09T13:06:04Z |
| institution | BVB |
| institution_GND | (DE-588)1065168780 |
| isbn | 9783662625200 3662625202 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032663081 |
| oclc_num | 1253630260 |
| open_access_boolean | |
| owner | DE-83 |
| owner_facet | DE-83 |
| physical | xi, 273 Seiten Illustrationen |
| publishDate | 2021 |
| publishDateSearch | 2021 |
| publishDateSort | 2021 |
| publisher | Berlin Springer Gabler |
| record_format | marc |
| spelling | Shikhman, Vladimir 1981- (DE-588)143928341 aut Mathematical foundations of big data analytics Vladimir Shikhman, David Müller Berlin Springer Gabler [2021] xi, 273 Seiten Illustrationen txt rdacontent n rdamedia nc rdacarrier Big Data (DE-588)4802620-7 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf Mathematisches Modell (DE-588)4114528-8 gnd rswk-swf Data Science (DE-588)1140936166 gnd rswk-swf Datenauswertung (DE-588)4131193-0 gnd rswk-swf Existence of a Ranking PageRank Recommendation Systems Neural Networks Classification Mathematical Models for Big Data Analytics Economic Applications of Big Data Analytics Clustering Analysis of Big Data Interdisciplinary Applications of Big Data Analytics Online Learning Decision Trees Sparse Recovery Linear Regression Big Data Statistics, general Data Analysis and Big Data Economics and Finance Hardcover, Softcover / Informatik, EDV/Datenkommunikation, Netzwerke (DE-588)4123623-3 Lehrbuch gnd-content Data Science (DE-588)1140936166 s Big Data (DE-588)4802620-7 s Datenanalyse (DE-588)4123037-1 s Datenauswertung (DE-588)4131193-0 s Mathematisches Modell (DE-588)4114528-8 s DE-604 Müller, David aut Springer-Verlag GmbH (DE-588)1065168780 pbl Erscheint auch als Online-Ausgabe 978-3-662-62521-7 X:MVB text/html http://deposit.dnb.de/cgi-bin/dokserv?id=60820c170240434589cf366eba69c7a3&prov=M&dok_var=1&dok_ext=htm Inhaltstext X:MVB http://www.springer.com/ DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032663081&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Shikhman, Vladimir 1981- Müller, David Mathematical foundations of big data analytics Big Data (DE-588)4802620-7 gnd Datenanalyse (DE-588)4123037-1 gnd Mathematisches Modell (DE-588)4114528-8 gnd Data Science (DE-588)1140936166 gnd Datenauswertung (DE-588)4131193-0 gnd |
| subject_GND | (DE-588)4802620-7 (DE-588)4123037-1 (DE-588)4114528-8 (DE-588)1140936166 (DE-588)4131193-0 (DE-588)4123623-3 |
| title | Mathematical foundations of big data analytics |
| title_auth | Mathematical foundations of big data analytics |
| title_exact_search | Mathematical foundations of big data analytics |
| title_exact_search_txtP | Mathematical foundations of big data analytics |
| title_full | Mathematical foundations of big data analytics Vladimir Shikhman, David Müller |
| title_fullStr | Mathematical foundations of big data analytics Vladimir Shikhman, David Müller |
| title_full_unstemmed | Mathematical foundations of big data analytics Vladimir Shikhman, David Müller |
| title_short | Mathematical foundations of big data analytics |
| title_sort | mathematical foundations of big data analytics |
| topic | Big Data (DE-588)4802620-7 gnd Datenanalyse (DE-588)4123037-1 gnd Mathematisches Modell (DE-588)4114528-8 gnd Data Science (DE-588)1140936166 gnd Datenauswertung (DE-588)4131193-0 gnd |
| topic_facet | Big Data Datenanalyse Mathematisches Modell Data Science Datenauswertung Lehrbuch |
| url | http://deposit.dnb.de/cgi-bin/dokserv?id=60820c170240434589cf366eba69c7a3&prov=M&dok_var=1&dok_ext=htm http://www.springer.com/ http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032663081&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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