Braga et al., 2008 - Google Patents
A GA-based feature selection and parameters optimization for support vector regression applied to software effort estimationBraga et al., 2008
View PDF- Document ID
- 3404251425822648170
- Author
- Braga P
- Oliveira A
- Meira S
- Publication year
- Publication venue
- Proceedings of the 2008 ACM symposium on Applied computing
External Links
Snippet
The precision of the estimation of the effort of software projects is very important for the competitiveness of software companies. Machine learning methods have recently been applied for this task, included methods based on support vector regression (SVR). This …
- 238000005457 optimization 0 title description 10
Classifications
-
- 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
- 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
- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/08—Learning methods
-
- 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
- G06N5/025—Extracting rules from data
-
- 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
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
- G06K9/6269—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches based on the distance between the decision surface and training patterns lying on the boundary of the class cluster, e.g. support vector machines
-
- 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
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6261—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation partitioning the feature space
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
-
- 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
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
- G06K9/6251—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on a criterion of topology preservation, e.g. multidimensional scaling, self-organising maps
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F7/00—Methods or arrangements for processing data by operating upon the order or content of the data handled
- G06F7/58—Random or pseudo-random number generators
-
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Braga et al. | A GA-based feature selection and parameters optimization for support vector regression applied to software effort estimation | |
Chakraborty et al. | Differential evolution and its applications in image processing problems: a comprehensive review | |
Heaton | Introduction to the math of neural networks (beta-1) | |
Gu et al. | Generating diverse and accurate classifier ensembles using multi-objective optimization | |
García-Martínez et al. | Role differentiation and malleable mating for differential evolution: an analysis on large-scale optimisation | |
Silva et al. | Novel approaches using evolutionary computation for sparse least square support vector machines | |
Pedrycz et al. | Identifying core sets of discriminatory features using particle swarm optimization | |
Zio et al. | Optimization of the inspection intervals of a safety system in a nuclear power plant by Multi-Objective Differential Evolution (MODE) | |
Hu et al. | Harmodt: Harmony multi-task decision transformer for offline reinforcement learning | |
Biza et al. | Out-of-sample tuning for causal discovery | |
Loni et al. | Learning activation functions for sparse neural networks | |
Chattopadhyay et al. | Feature selection using differential evolution with binary mutation scheme | |
Zhang et al. | User Purchase Intention Prediction Based on Improved Deep Forest. | |
Ding et al. | Intelligent optimization methods for high-dimensional data classification for support vector machines | |
Soares et al. | A genetic algorithm for designing neural network ensembles | |
Brito et al. | Generating neural networks with optimal features through particle swarm optimization | |
Shem-Tov et al. | Deep neural crossover: A multi-parent operator that leverages gene correlations | |
Rosales-Perez et al. | Bias and Variance Optimization for SVMs Model Selection. | |
Chan et al. | A two-phase evolutionary algorithm for multiobjective mining of classification rules | |
Márquez-Grajales et al. | An adaptive symbolic discretization scheme for the classification of temporal datasets using NSGA-II | |
Shem-Tov et al. | Deep neural crossover | |
Marrero et al. | Learning descriptors for novelty-search based instance generation via meta-evolution | |
Silva et al. | Sparse least squares support vector machines via genetic algorithms | |
Silva et al. | A genetic algorithms-based lssvm classifier for fixed-size set of support vectors | |
Amarasinghe et al. | Evolutionary Multi-Objective Optimisation for Fairness-Aware Self Adjusting Memory Classifiers in Data Streams |