Granitto et al., 2002 - Google Patents
Aggregation algorithms for neural network ensemble constructionGranitto et al., 2002
View PDF- Document ID
- 15157122345757912975
- Author
- Granitto P
- Verdes P
- Navone H
- Ceccatto H
- Publication year
- Publication venue
- VII Brazilian Symposium on Neural Networks, 2002. SBRN 2002. Proceedings.
External Links
Snippet
How to generate and aggregate base learners to have optimal ensemble generalization capabilities is an important questions in building composite regression/classification machines. We present here an evaluation of several algorithms for artificial neural networks …
- 230000001537 neural 0 title abstract description 12
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/04—Architectures, e.g. interconnection topology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/12—Computer systems based on biological models using genetic models
- G06N3/126—Genetic algorithms, i.e. information processing using digital simulations of the genetic system
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/04—Inference methods or devices
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6279—Classification techniques relating to the number of classes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Frank et al. | Using model trees for classification | |
St-Aubin et al. | APRICODD: Approximate policy construction using decision diagrams | |
Zhu et al. | Active learning from stream data using optimal weight classifier ensemble | |
Even-Dar et al. | Action elimination and stopping conditions for the multi-armed bandit and reinforcement learning problems. | |
Zhang et al. | Draft & verify: Lossless large language model acceleration via self-speculative decoding | |
He et al. | Filter pruning by switching to neighboring CNNs with good attributes | |
Valentini et al. | Low bias bagged support vector machines | |
Helmbold et al. | Relative loss bounds for single neurons | |
Cao et al. | Fuzziness-based online sequential extreme learning machine for classification problems | |
Zhai et al. | Boosted cvar classification | |
Wang et al. | On the margin explanation of boosting algorithms. | |
Qiu | Bare bones particle swarm optimization with adaptive chaotic jump for feature selection in classification | |
Vural et al. | Minimax optimal algorithms for adversarial bandit problem with multiple plays | |
Granitto et al. | Aggregation algorithms for neural network ensemble construction | |
Ganea et al. | Breaking the softmax bottleneck via learnable monotonic pointwise non-linearities | |
Navone et al. | A learning algorithm for neural network ensembles | |
AU753822B2 (en) | N-tuple or ram based neural network classification system and method | |
Calhoun et al. | Random forest with acceptance–rejection trees | |
Feldman | Robustness of Evolvability. | |
Nguyen et al. | An online variational inference and ensemble based multi-label classifier for data streams | |
Šíma et al. | On the computational complexity of binary and analog symmetric Hopfield nets | |
Estruch et al. | Bagging decision multi-trees | |
Cinà et al. | AttackBench: Evaluating Gradient-based Attacks for Adversarial Examples | |
WO1999040521A1 (en) | N-tuple or ram based neural network classification system and method | |
Mesterharm | Tracking linear-threshold concepts with winnow |