Lie group machine learning:
Gespeichert in:
| Beteiligte Personen: | , , |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Berlin ; Boston
De Gruyter
[2019]
|
| Schlagwörter: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030722007&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Umfang: | XVI, 517 Seiten Illustrationen, Diagramme |
| ISBN: | 9783110500684 |
Internformat
MARC
| LEADER | 00000nam a2200000 c 4500 | ||
|---|---|---|---|
| 001 | BV045335224 | ||
| 003 | DE-604 | ||
| 005 | 20240222 | ||
| 007 | t| | ||
| 008 | 181204s2019 gw a||| |||| 00||| eng d | ||
| 015 | |a 18,N28 |2 dnb | ||
| 016 | 7 | |a 1162290366 |2 DE-101 | |
| 020 | |a 9783110500684 |c hbk. |9 978-3-11-050068-4 | ||
| 035 | |a (OCoLC)1077755145 | ||
| 035 | |a (DE-599)DNB1162290366 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 044 | |a gw |c XA-DE-BE | ||
| 049 | |a DE-29T |a DE-91G |a DE-20 |a DE-703 |a DE-11 | ||
| 084 | |a ST 301 |0 (DE-625)143651: |2 rvk | ||
| 084 | |a ST 302 |0 (DE-625)143652: |2 rvk | ||
| 084 | |a SK 260 |0 (DE-625)143227: |2 rvk | ||
| 084 | |a ST 300 |0 (DE-625)143650: |2 rvk | ||
| 084 | |a DAT 708f |2 stub | ||
| 084 | |a 004 |2 sdnb | ||
| 084 | |a MAT 225f |2 stub | ||
| 100 | 1 | |a Li, Fanzhang |e Verfasser |0 (DE-588)1148670343 |4 aut | |
| 245 | 1 | 0 | |a Lie group machine learning |c Li Fanzhang, Zhang Li, Zhang Zhao |
| 264 | 1 | |a Berlin ; Boston |b De Gruyter |c [2019] | |
| 264 | 4 | |c © 2019 | |
| 300 | |a XVI, 517 Seiten |b Illustrationen, Diagramme | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 650 | 0 | 7 | |a Lie-Gruppe |0 (DE-588)4035695-4 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Deep Learning |0 (DE-588)1135597375 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Kognitiver Prozess |0 (DE-588)4140177-3 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |D s |
| 689 | 0 | 1 | |a Lie-Gruppe |0 (DE-588)4035695-4 |D s |
| 689 | 0 | 2 | |a Kognitiver Prozess |0 (DE-588)4140177-3 |D s |
| 689 | 0 | 3 | |a Deep Learning |0 (DE-588)1135597375 |D s |
| 689 | 0 | |5 DE-604 | |
| 700 | 1 | |a Zhang, Li |e Verfasser |0 (DE-588)1148670602 |4 aut | |
| 700 | 1 | |a Zhang, Zhao |e Verfasser |0 (DE-588)1148670947 |4 aut | |
| 710 | 2 | |a Walter de Gruyter GmbH & Co. KG |0 (DE-588)10095502-2 |4 pbl | |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe, EPUB |z 978-3-11-049807-3 |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe, PDF |z 978-3-11-049950-6 |
| 856 | 4 | 2 | |m DNB Datenaustausch |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030722007&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-030722007 | |
Datensatz im Suchindex
| DE-BY-TUM_call_number | 0102 DAT 708f 2019 A 440 |
|---|---|
| DE-BY-TUM_katkey | 2380519 |
| DE-BY-TUM_location | 01 |
| DE-BY-TUM_media_number | 040008852935 |
| _version_ | 1823989033819176960 |
| adam_text |
CONTENTS
PREFACE* V
1 LIE GROUP MACHINE LEARNING MODEL * 1
1.1 INTRODUCTION * 1
1.2 CONCEPTS OF LIE GROUP MACHINE LEARNING* 1
1.3 ALGEBRAIC MODEL OF LIE GROUP MACHINE LEARNING* 10
1.3.1 LIE ALGEBRAS * 10
1.3.2 ONE-PARAMETER SUBGROUP * 11
1.3.3 ALGEBRAIC MODEL* 12
1.4 GEOMETRIC MODEL OF LIE GROUP MACHINE LEARNING* 13
1.5 AXIOM HYPOTHESIS OF LIE GROUP MACHINE LEARNING * 15
1.6 GEOMETRIC LEARNING ALGORITHM FOR DYNKIN GRAPHS IN LIE GROUP
MACHINE LEARNING* 26
1.6.1 OVERVIEW OF DYNKIN GRAPHS IN LIE GROUP MACHINE LEARNING* 27
1.6.2 CLASSIFICATION ALGORITHM OF DYNKIN DIAGRAMS IN LIE GROUP
MACHINE LEARNING * 29
1.7 LINEAR CLASSIFIER DESIGN OF LIE GROUP MACHINE LEARNING* 33
1.7.1 LINEAR CLASSIFIER DESIGN OF LIE GROUP MACHINE LEARNING* 33
1.7.2 LIE GROUP MACHINE LEARNING SO(3) CLASSIFIER* 34
1.7.3 CLASSIFICATION OF TEXT BASED ON LIE GROUP MACHINE LEARNING * 35
1.8 CHAPTER SUMMARY * 37
2 LIE GROUP SUBSPACE ORBIT GENERATION LEARNING* 39
2.1 BASIC CONCEPTS OF PARTIAL ORDER AND LATTICE IN LML * 39
2.1.1 BASIC CONCEPTS * 39
2.1.2 PARTIALLY ORDERED SET IN LML * 43
2.1.3 MOEBIUS FUNCTION ON LOCAL FINITE PARTIAL ORDER IN LML * 46
2.1.4 GAUSSIAN COEFFICIENTS AND GAUSSIAN POLYNOMIALS IN LML * 49
2.1.5 LATTICES IN LML * 54
2.2 LML SUBSPACE ORBIT GENERATING LATTICE LEARNING ALGORITHM * 56
2.2.1 LML SUBSPACE ORBIT GENERATION LATTICE * 56
2.2.2 ORBIT GENERATION LEARNING ALGORITHM FOR LML SUBSPACE * 59
2.3 LML LEARNING SUBSPACE ORBIT GENERATION LATTICE LEARNING ALGORITHM
UNDER
THE ACTION OF THE GENERAL LINEAR GROUP GLN(F*) * 71
2.3.1 PROBLEM DESCRIPTION * 71
2.3.2 LML LEARNING SUBSPACE ORBIT GENERATION LATTICE UNDER THE ACTION
OF THE GENERAL LINEAR GROUP GLNF* * 72
2.3.3 LEARNING ALGORITHMS AND EXAMPLES * 76
2.4 SUMMARY * 79
3
3.1
3.2
3.2.1
3.2.2
3.2.3
3.2.4
3.3
3.3.1
3.3.2
3.3.3
3.4
3.4.1
3.4.2
3.5
4
4.1
4.2
4.2.1
4.2.2
4.2.3
4.3
4.3.1
4.3.2
4.3.3
4.3.4
4.3.5
4.4
5
5.1
SYMPLECTIC GROUP LEARNING
* 81
PROBLEM PRESENTATION * 81
DESIGN OF THE SYMPLECTIC GROUP CLASSIFIER
IN LIE GROUP MACHINE LEARNING * 88
SYMPLECTIC GROUP CLASSIFIER DESCRIPTION * 88
DESIGN METHOD OF SYMPLECTIC GROUP CLASSIFIER * 94
DESIGN OF SYMPLECTIC GROUP CLASSIFIER IN FACE RECOGNITION * 100
SYMPLECTIC GROUP CLASSIFIER DESIGN FOR CLASSIFICATION PROCESS
OF DATA SET * 103
SYMPLECTIC GROUP CLASSIFIER ALGORITHM
IN LIE GROUP MACHINE LEARNING * 105
SYMPLECTIC GROUP CLASSIFIER ALGORITHM * 105
VERIFICATION OF SYMPLECTIC GROUP CLASSIFIER ALGORITHM IN FACE
RECOGNITION * 107
VERIFICATION OF SYMPLECTIC GROUP CLASSIFIER ALGORITHM IN DATA SET
CLASSIFICATION * 109
APPLICATION EXAMPLE * 111
PROCESSING OF IMAGES UNDER SYMPLECTIC MATRIX * 112
INSTANCE VALIDATION * 116
SUMMARY * 119
QUANTUM GROUP LEARNING
* 123
PROBLEM PRESENTATION * 123
CONSTRUCTION METHOD OF QUANTUM GROUP CLASSIFIER
IN LIE GROUP MACHINE LEARNING* 124
PROBLEM DESCRIPTION * 124
CONSTRUCTION OF QUANTUM GROUP CLASSIFIER IN LIE GROUP MACHINE
LEARNING * 125
DNA SEQUENCE CLASSIFICATION BASED ON QUANTUM GROUP CLASSIFIER
IN LIE GROUP MACHINE LEARNING * 128
APPLICATION OF QUANTUM GROUP LEARNING ALGORITHM
IN MOLECULAR DOCKING* 132
INTRODUCTION TO MOLECULAR DOCKING ALGORITHM * 132
MOLECULAR DOCKING DESIGN MODEL BASED ON QUANTUM GROUP * 135
MOLECULAR MATCHING ALGORITHM BASED ON QUANTUM GROUP
GENERATORS * 138
MOLECULAR DOCKING SIMULATION BASED ON QUANTUM GROUP * 139
EXPERIMENTAL RESULTS AND ANALYSIS * 140
SUMMARY * 144
LIE GROUP FIBRE BUNDLE LEARNING* 147
PROBLEM PRESENTATION * 147
5.2
5.2.1
5.2.2
5.2.3
5.3
5.3.1
5.3.2
5.4
6
6.1
6.1.1
6
.
1.2
6.2
6
.
2.1
6
.
2.2
6.2.3
6.2.4
6.3
6.3.1
6.3.2
6.3.3
6.4
6.4.1
6.4.2
6.4.3
6.4.4
6.5
7
7.1
7.2
7.2.1
7.2.2
7.2.3
7.2.4
7.2.5
7.3
7.3.1
7.3.2
7.3.3
FIBRE BUNDLE MODEL * 148
EXPRESSION OF FIBRE BUNDLES IN MANIFOLD LEARNING * 148
TANGENT BUNDLE MODEL FOR MANIFOLD LEARNING* 150
MAIN FIBRE BUNDLE MODEL * 152
FIBRE BUNDLE LEARNING ALGORITHM * 153
VECTOR REDUCTION ALGORITHM BASED ON LOCAL PRINCIPAL DIRECTION OF
TANGENT * 154
MAIN LINK CURVE CONSTRUCTION ALGORITHM BASED ON TANGENT CONTACT* 156
SUMMARY * 162
LIE GROUP COVERING LEARNING * 165
THEORY OF LIE GROUP MACHINE LEARNING COVERING ALGORITHM * 165
LINEAR REPRESENTATION OF A GROUP * 165
BASIC PROPERTIES OF THE LIE GROUP * 167
SIMPLY CONNECTED COVERING ALGORITHM OF THE LIE GROUP * 173
RESEARCH STATUS OF ALGORITHM BASED ON COVERING IDEA * 173
SIMPLY CONNECTED COVERING OF LIE GROUP MACHINE LEARNING* 175
ALGORITHM DESIGN * 178
EXAMPLE APPLICATION ANALYSIS * 179
MULTIPLY CONNECTED COVERING ALGORITHM
OF LIE GROUP MACHINE LEARNING* 181
LML MULTIPLY CONNECTED COVERING MODEL * 182
MULTIPLY CONNECTED COVERING ALGORITHM DESIGN * 184
APPLICATIONS * 187
APPLICATION OF THE COVERING ALGORITHM IN MOLECULAR DOCKING* 189
INTRODUCTION TO THE MOLECULAR DOCKING ALGORITHM * 189
MATHEMATICAL MODEL AND EVALUATION FUNCTION OF MOLECULAR
DOCKING * 191
COVERING STRATEGY AND IMPLEMENTATION OF MOLECULAR DOCKING * 193
EXPERIMENTAL RESULTS AND ANALYSIS * 199
SUMMARY * 204
LIE GROUP DEEP STRUCTURE LEARNING
* 207
INTRODUCTION * 207
LIE GROUP DEEP STRUCTURE LEARNING * 209
DEEP STRUCTURE LEARNING * 209
CONSTRUCT DEEP STRUCTURE MODEL * 210
DEEP STRUCTURE LEARNING ALGORITHM * 212
LIE GROUP DEEP STRUCTURE LEARNING ALGORITHM * 214
EXPERIMENT ANALYSIS * 218
LIE GROUP LAYERED LEARNING ALGORITHM * 220
SINGULAR VALUE FEATURE EXTRACTION * 221
LAYERED LEARNING ALGORITHM * 223
EXPERIMENT AND ANALYSIS * 225
7.4
7.4.1
7.4.2
7.4.3
7.4.4
7.5
8
8.1
8 .1.1
8 .1.2
8.2
8
.
2.1
8
.
2.2
8.2.3
8.3
8.3.1
8.3.2
8.3.3
8.4
8.4.1
8.4.2
8.4.3
8.5
9
9.1
9.1.1
9.1.2
9.1.3
9.1.4
9.2
9.2.1
9.2.2
9.2.3
9.3
9.4
LIE GROUP DEEP STRUCTURE HEURISTIC LEARNING* 227
HEURISTIC LEARNING ALGORITHM * 227
A* ALGORITHM
----
228
LIE GROUP DEEP STRUCTURE HEURISTIC LEARNING ALGORITHM * 229
EXPERIMENT AND ANALYSIS * 230
SUMMARY * 231
LIE GROUP SEMI-SUPERVISED LEARNING * 235
INTRODUCTION * 235
RESEARCH STATUS OF SEMI-SUPERVISED LEARNING * 235
QUESTIONS RAISED * 243
SEMI-SUPERVISED LEARNING MODEL BASED ON THE LIE GROUP * 244
REPRESENTATION OF THE LIE GROUP IN SEMI-SUPERVISED STUDY * 244
SEMI-SUPERVISED LEARNING MODEL BASED ON LIE GROUP
ALGEBRA STRUCTURE * 245
SEMI-SUPERVISED LEARNING MODEL BASED ON THE GEOLOGICAL STRUCTURE
OF THE LIE GROUP
----
247
SEMI-SUPERVISED LEARNING ALGORITHM BASED ON A LINEAR LIE GROUP * 249
THE GENERAL LINEAR GROUP * 250
SEMI-SUPERVISED LEARNING ALGORITHM BASED
ON THE LINEAR LIE GROUP * 253
EXPERIMENT* 256
SEMI-SUPERVISED LEARNING ALGORITHM BASED
ON THE PARAMETER LIE GROUP * 259
SAMPLE DATA REPRESENTATION * 259
SEMI-SUPERVISED LEARNING ALGORITHM BASED ON THE PARAMETER
LIE GROUP * 260
EXPERIMENT
----
264
SUMMARY* 269
LIE GROUP KERNEL LEARNING* 275
MATRIX GROUP LEARNING ALGORITHM * 275
RELATED BASIC CONCEPTS
----
275
MATRIX GROUP * 275
THE LEARNING ALGORITHM OF THE MATRIX GROUP * 280
CASES ANALYSIS * 281
GAUSSIAN DISTRIBUTION ON THE LIE GROUP * 284
GAUSSIAN DISTRIBUTION OF R+ * 285
GAUSSIAN DISTRIBUTION OF 50(2) * 285
GAUSSIAN DISTRIBUTION OF 50(3) * 287
CALCULATION OF THE LIE GROUP INNER MEAN VALUE * 288
LIE-MEAN LEARNING ALGORITHM * 291
9.4.1 FLDA ALGORITHM
----
292
9.4.2 FISHER MAPPING IN LIE GROUP SPACE * 292
9.4.3 LIE-FISHER DISCRIMINANT ANALYSIS * 294
9.5 NUCLEAR LEARNING ALGORITHM OF THE LIE GROUP * 298
9.5.1 PRINCIPLE OF THE SVM ALGORITHM * 299
9.5.2 THE PRINCIPLE OF KFDA * 300
9.5.3 KERNEL * 302
9.5.4 KERNEL OF THE LIE GROUP * 303
9.5.5 KLIEDA ALGORITHM BASED ON THE LIE GROUP KERNEL FUNCTION * 305
9.6 CASE APPLICATION * 306
9.6.1 EXPERIMENTAL ANALYSIS OF THE LIE-FISHER ALGORITHM * 306
9.6.2 ARTIFICIAL DATA SET * 307
9.6.3 HANDWRITING RECOGNITION * 309
9.6.4 COVARIANCE LIE GROUP CHARACTERISTIC
OF THE LIE-FISHER HANDWRITING CLASSIFICATION * 311
9.7 SUMMARY * 314
10 TENSOR LEARNING* 319
10.1 DATA REDUCTION BASED ON TENSOR METHODS * 319
10.1.1 GLRAM
-------
319
10.1.2 HOOI
-------
320
10.1.3 2DPCA
-------
320
10.1.4 CUBESVD
----
321
10.1.5 TSA
-------
322
10.1.6 RELATED PROBLEM * 322
10.2 DATA REDUCTION MODEL BASED ON TENSOR FIELDS * 323
10.2.1 TENSOR FIELD ON A MANIFOLD * 323
10.2.2 REDUCTION MODEL BASED ON THE TENSOR FIELD * 325
10.2.3 DESIGN OF DATA REDUCTION ALGORITHM BASED ON THE TENSOR FIELD *
327
10.2.4 EXPERIMENT* 330
10.3 THE LEARNING MODEL AND ALGORITHM BASED ON THE TENSOR FIELD * 332
10.3.1 LEARNING MODEL BASED ON THE TENSOR FIELD * 332
10.3.2 TENSOR BUNDLE LEARNING ALGORITHM * 335
10.3.3 CLASSIFICATION MODEL BASED ON THE TENSOR FIELD * 337
10.3.4 CLASSIFICATION ALGORITHM BASED ON THE TENSOR FIELD * 342
10.4 SUMMARY * 344
11 FRAME BUNDLE CONNECTION LEARNING * 347
11.1 LONGITUDINAL SPACE LEARNING MODEL BASED ON FRAME BUNDLE * 347
11.2 LONGITUDINAL SPACE CONNECTION LEARNING MODEL
BASED ON FRAME BUNDLE * 350
11.3 HORIZONTAL SPACE CONNECTION LEARNING MODEL
BASED ON FRAME BUNDLE * 352
11.4 RELATED APPLICATIONS * 353
11.5 SUMMARY * 355
12 SPECTRAL ESTIMATION LEARNING * 357
12.1 CONCEPT AND DEFINITION OF SPECTRAL ESTIMATION * 357
12.1.1 RESEARCH BACKGROUND OF THE SPECTRAL ESTIMATION METHOD * 357
12.1.2 CONCEPT AND DEFINITION OF SPECTRAL ESTIMATION * 357
12.1.3 RESEARCH PROGRESS IN LEARNING METHODS OF SPECTRAL ESTIMATION *
358
12.2 RELEVANT THEORETICAL BASIS * 359
12.2.1 HOWTO CONSTRUCT A SIMILARITY MATRIX * 360
12.2.2 HOW TO CHOOSE THE APPROPRIATE LAPLACIAN MATRIX * 362
12.2.3 SELECTING THE APPROPRIATE FEATURE VECTOR * 362
12.2.4 DETERMINING THE NUMBER OF CLUSTERS * 364
12.3 SYNCHRONOUS SPECTRUM ESTIMATION LEARNING ALGORITHM * 365
12.3.1 GRAPH OPTIMISATION CRITERION FOR LOCALLY PRESERVING MAPPINGS *
365
12.3.2 ASYNCHRONOUS SPECTRUM ESTIMATION LEARNING MODEL * 366
12.3.3 SYNCHRONOUS SPECTRUM ESTIMATION LEARNING ALGORITHM * 368
12.3.4 CASE VERIFICATION * 369
12.4 THE COMPARISON PRINCIPLE OF IMAGE FEATURE MANIFOLDS * 371
12.4.1 TOPOLOGICAL SPHERICAL THEOREM * 372
12.4.2 POLARISATION THEOREM OF IMAGE FEATURE MANIFOLDS * 374
12.4.3 MANIFOLD DIMENSIONALITY REDUCTION ALGORITHM * 375
12.5 SPECTRAL ESTIMATION LEARNING ALGORITHM FOR TOPOLOGICAL INVARIANCE
OF IMAGE FEATURE MANIFOLDS * 376
12.5.1 SPECTRAL ESTIMATION LEARNING ALGORITHM FOR THE TOPOLOGICAL
INVARIANCE
OF IMAGE FEATURE MANIFOLDS * 377
12.5.2 ALGORITHM ANALYSIS * 377
12.5.3 EXAMPLE ANALYSIS * 378
12.6 CLUSTERING ALGORITHM BASED ON THE TOPOLOGICAL INVARIANCE SPECTRAL
ESTIMATION OF IMAGE FEATURE MANIFOLDS * 380
12.7 SUMMARY * 380
13 FINSLER GEOMETRIC LEARNING * 383
13.1 BASIC CONCEPT* 383
13.1.1 RIEMANN MANIFOLD * 383
13.1.2 FINSLER GEOMETRY * 384
13.2 KNN ALGORITHM BASED ON THE FINSLER METRIC * 384
13.2.1 K NEAREST NEIGHBOUR ALGORITHM * 384
13.2.2 KNN ALGORITHM BASED ON THE FINSLER METRIC * 386
13.2.3 EXPERIMENTAL RESULTS AND ANALYSIS * 388
13.3 GEOMETRIE LEARNING ALGORITHM BASED ON THE FINSLER METRIC * 390
13.3.1 SUPERVISED MANIFOLD LEARNING ALGORITHM * 391
13.3.2 FINSLER GEOMETRIC LEARNING ALGORITHM * 393
13.4 SUMMARY * 398
14 HOMOLOGY BOUNDARY LEARNING* 401
14.1 BOUNDARY LEARNING ALGORITHM * 401
14.1.1 TANGENT VECTOR QUANTISATION (TVQ) ALGORITHM * 401
14.1.2 REGULARISED LARGE MARGINAL CLASSIFIER (RLMC) * 402
14.1.3 BOUNDARY DETECTION ALGORITHM BASED ON THE BOUNDARY MARKOV RANDOM
FIELD AND BOLTZMANN MACHINE * 402
14.1.4 FUZZY EDGE DETECTION ALGORITHM BASED ON THE QUALIFICATION
FUNCTION * 403
14.2 EDGE DIVISION METHOD BASED ON HOMOLOGICAL ALGEBRA * 404
14.2.1 BASIC CONCEPTS OF HOMOLOGICAL ALGEBRA * 404
14.2.2 MAPPING OF HOMOTOPY AND THE SPACE OF HOMOTOPY * 405
14.2.3 COHOMOLOGY EDGE ALGORITHM * 406
14.2.4 CELL HOMOLOGY EDGE ALGORITHM * 408
14.3 DESIGN AND ANALYSIS OF HOMOLOGY EDGE LEARNING ALGORITHM * 412
14.4 SUMMARY * 413
15 CATEGORY REPRESENTATION LEARNING
* 415
15.1 INTRODUCTION * 415
15.1.1 RESEARCH BACKGROUND * 415
15.1.2 THE RELATION BETWEEN CATEGORY THEORY AND COMPUTER SCIENCE * 418
15.1.3 BASIC CONCEPTS OF CATEGORY THEORY* 420
15.1.4 PROPOSED PROBLEM * 424
15.2 CATEGORY REPRESENTATION OF LEARNING EXPRESSIONS * 425
15.2.1 CATEGORY REPRESENTATION OF MACHINE LEARNING SYSTEMS * 425
15.2.2 CATEGORY REPRESENTATION OF LEARNING EXPRESSIONS * 429
15.2.3 CATEGORY REPRESENTATION OF THE LEARNING EXPRESSION FUNCTOR* 431
15.2.4 NATURAL TRANSFORMATION * 432
15.3 MAPPING MECHANISM FOR LEARNING EXPRESSIONS * 433
15.3.1 ABSTRACT CONCEPT OF AN EXPRESSION * 433
15.3.2 MAPPING MECHANISM BETWEEN EXPRESSIONS * 436
15.4 CLASSIFIER DESIGN FOR LEARNING EXPRESSION MAPPING MECHANISM * 441
15.4.1 CLASSIFIER ALGORITHM * 442
15.4.2 CLASSIFIER BASED ON THE LEARNING EXPRESSION MAPPING
MECHANISM * 445
15.4.3 EXAMPLE ANALYSIS AND RESULTS * 448
15.5 EXAMPLE ANALYSIS * 451
15.5.1 INSTANCE ANALYSIS OF LEARNING EXPRESSION MAPPING* 451
15.5.2 CASE ANALYSIS OF IMAGE RECOGNITION
----
459
15.6 SUMMARY* 462
16 NEUROMORPHIC SYNERGY LEARNING* 465
16.1 INTRODUCTION
----
465
16.2 CORE SCIENTIFIC PROBLEMS
----
469
16.3 LIE GROUP COGNITIVE THEORY FRAMEWORK* 471
16.4 NEUROMORPHIC SYNERGY LEARNING THEORETICAL FRAMEWORK
----
475
16.4.1 SYMBOL GROUNDING LEARNING* 477
16.4.2 BIDIRECTIONAL SYNERGY LEARNING
----
478
16.4.3 AFFORDANCE LEARNING* 479
16.4.4 MULTI-SCALE SYNERGY LEARNING
-----
480
16.5 DESIGN OF A NEUROMORPHIC SYNERGY LEARNING VERIFICATION PLATFORM
----
482
16.6 SUMMARY
----
483
17 APPENDIX
----
485
17.1 TOPOLOGICAL GROUP
----
485
17.2 CONCEPT OF DIFFERENTIAL GEOMETRY
----
488
17.3 MANIFOLD LEARNING ALGORITHM
-----
490
17.3.1 LOCAL LINEAR EMBEDDING (LLE)
----
490
17.3.2 ISOMETRIC MAPPING (ISOMAP)
-----
491
17.3.3 HORIZONTAL LINEAR EMBEDDING (HLLE) * 492
17.3.4 LAPLACIAN EIGENMAP
----
493
17.3.5 LOCAL TANGENCY SPACE ARRANGEMENT (LTSA)
----
494
17.4 BASIC CONCEPT AND NATURE OF SYMPLECTIC GROUP* 494
17.5 BASIC CONCEPTS OF QUANTUM GROUPS
----
499
17.5.1 IMAGE DESCRIPTION OF THE QUANTUM GROUP
----
499
17.5.2 DEFINITION AND DECISION ALGORITHM OF QUANTUM GROUP* 500
17.5.3 QUANTISATION
----
503
17.5.4 REPRESENTATION OF QUANTUM GROUPS
----
505
17.6 FIBRE BUNDLE
----
506
AUTHORS
----
513
IN DEX* 515 |
| any_adam_object | 1 |
| author | Li, Fanzhang Zhang, Li Zhang, Zhao |
| author_GND | (DE-588)1148670343 (DE-588)1148670602 (DE-588)1148670947 |
| author_facet | Li, Fanzhang Zhang, Li Zhang, Zhao |
| author_role | aut aut aut |
| author_sort | Li, Fanzhang |
| author_variant | f l fl l z lz z z zz |
| building | Verbundindex |
| bvnumber | BV045335224 |
| classification_rvk | ST 301 ST 302 SK 260 ST 300 |
| classification_tum | DAT 708f MAT 225f |
| ctrlnum | (OCoLC)1077755145 (DE-599)DNB1162290366 |
| discipline | Informatik Mathematik |
| format | Book |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>00000nam a2200000 c 4500</leader><controlfield tag="001">BV045335224</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20240222</controlfield><controlfield tag="007">t|</controlfield><controlfield tag="008">181204s2019 gw a||| |||| 00||| eng d</controlfield><datafield tag="015" ind1=" " ind2=" "><subfield code="a">18,N28</subfield><subfield code="2">dnb</subfield></datafield><datafield tag="016" ind1="7" ind2=" "><subfield code="a">1162290366</subfield><subfield code="2">DE-101</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9783110500684</subfield><subfield code="c">hbk.</subfield><subfield code="9">978-3-11-050068-4</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1077755145</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DNB1162290366</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="044" ind1=" " ind2=" "><subfield code="a">gw</subfield><subfield code="c">XA-DE-BE</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-29T</subfield><subfield code="a">DE-91G</subfield><subfield code="a">DE-20</subfield><subfield code="a">DE-703</subfield><subfield code="a">DE-11</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 301</subfield><subfield code="0">(DE-625)143651:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 302</subfield><subfield code="0">(DE-625)143652:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">SK 260</subfield><subfield code="0">(DE-625)143227:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 300</subfield><subfield code="0">(DE-625)143650:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">DAT 708f</subfield><subfield code="2">stub</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">004</subfield><subfield code="2">sdnb</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">MAT 225f</subfield><subfield code="2">stub</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Li, Fanzhang</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)1148670343</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Lie group machine learning</subfield><subfield code="c">Li Fanzhang, Zhang Li, Zhang Zhao</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Berlin ; Boston</subfield><subfield code="b">De Gruyter</subfield><subfield code="c">[2019]</subfield></datafield><datafield tag="264" ind1=" " ind2="4"><subfield code="c">© 2019</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">XVI, 517 Seiten</subfield><subfield code="b">Illustrationen, Diagramme</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Lie-Gruppe</subfield><subfield code="0">(DE-588)4035695-4</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Deep Learning</subfield><subfield code="0">(DE-588)1135597375</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Kognitiver Prozess</subfield><subfield code="0">(DE-588)4140177-3</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Maschinelles Lernen</subfield><subfield code="0">(DE-588)4193754-5</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Maschinelles Lernen</subfield><subfield code="0">(DE-588)4193754-5</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Lie-Gruppe</subfield><subfield code="0">(DE-588)4035695-4</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="2"><subfield code="a">Kognitiver Prozess</subfield><subfield code="0">(DE-588)4140177-3</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="3"><subfield code="a">Deep Learning</subfield><subfield code="0">(DE-588)1135597375</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhang, Li</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)1148670602</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhang, Zhao</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)1148670947</subfield><subfield code="4">aut</subfield></datafield><datafield tag="710" ind1="2" ind2=" "><subfield code="a">Walter de Gruyter GmbH & Co. KG</subfield><subfield code="0">(DE-588)10095502-2</subfield><subfield code="4">pbl</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Online-Ausgabe, EPUB</subfield><subfield code="z">978-3-11-049807-3</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Online-Ausgabe, PDF</subfield><subfield code="z">978-3-11-049950-6</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="m">DNB Datenaustausch</subfield><subfield code="q">application/pdf</subfield><subfield code="u">http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030722007&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA</subfield><subfield code="3">Inhaltsverzeichnis</subfield></datafield><datafield tag="943" ind1="1" ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-030722007</subfield></datafield></record></collection> |
| id | DE-604.BV045335224 |
| illustrated | Illustrated |
| indexdate | 2025-02-13T07:00:23Z |
| institution | BVB |
| institution_GND | (DE-588)10095502-2 |
| isbn | 9783110500684 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-030722007 |
| oclc_num | 1077755145 |
| open_access_boolean | |
| owner | DE-29T DE-91G DE-BY-TUM DE-20 DE-703 DE-11 |
| owner_facet | DE-29T DE-91G DE-BY-TUM DE-20 DE-703 DE-11 |
| physical | XVI, 517 Seiten Illustrationen, Diagramme |
| publishDate | 2019 |
| publishDateSearch | 2019 |
| publishDateSort | 2019 |
| publisher | De Gruyter |
| record_format | marc |
| spellingShingle | Li, Fanzhang Zhang, Li Zhang, Zhao Lie group machine learning Lie-Gruppe (DE-588)4035695-4 gnd Deep Learning (DE-588)1135597375 gnd Kognitiver Prozess (DE-588)4140177-3 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4035695-4 (DE-588)1135597375 (DE-588)4140177-3 (DE-588)4193754-5 |
| title | Lie group machine learning |
| title_auth | Lie group machine learning |
| title_exact_search | Lie group machine learning |
| title_full | Lie group machine learning Li Fanzhang, Zhang Li, Zhang Zhao |
| title_fullStr | Lie group machine learning Li Fanzhang, Zhang Li, Zhang Zhao |
| title_full_unstemmed | Lie group machine learning Li Fanzhang, Zhang Li, Zhang Zhao |
| title_short | Lie group machine learning |
| title_sort | lie group machine learning |
| topic | Lie-Gruppe (DE-588)4035695-4 gnd Deep Learning (DE-588)1135597375 gnd Kognitiver Prozess (DE-588)4140177-3 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Lie-Gruppe Deep Learning Kognitiver Prozess Maschinelles Lernen |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030722007&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT lifanzhang liegroupmachinelearning AT zhangli liegroupmachinelearning AT zhangzhao liegroupmachinelearning AT walterdegruytergmbhcokg liegroupmachinelearning |
Inhaltsverzeichnis
Paper/Kapitel scannen lassen
Paper/Kapitel scannen lassen
Teilbibliothek Mathematik & Informatik
| Signatur: |
0102 DAT 708f 2019 A 440 Lageplan |
|---|---|
| Exemplar 1 | Ausleihbar Am Standort |