Difference between revisions of "Repositories & Other Algorithms"
m (BPeat moved page Algorithms to Repositories & Other Algorithms without leaving a redirect) |
|||
| Line 1: | Line 1: | ||
[http://www.youtube.com/results?search_query=open+source+code+sourceforge+github+artificial+intelligence+deep+learning Youtube search...] | [http://www.youtube.com/results?search_query=open+source+code+sourceforge+github+artificial+intelligence+deep+learning Youtube search...] | ||
| + | Wikipedia pages... | ||
| + | * [http://en.wikipedia.org/wiki/Almeida%E2%80%93Pineda_recurrent_backpropagation Almeida–Pineda recurrent backpropagation] | ||
| + | * [http://en.wikipedia.org/wiki/ALOPEX ALOPEX] | ||
| + | * [http://en.wikipedia.org/wiki/Backpropagation Backpropagation] | ||
| + | * [http://en.wikipedia.org/wiki/Bootstrap_aggregating Bootstrap aggregating] | ||
| + | * [http://en.wikipedia.org/wiki/CN2_algorithm CN2 algorithm] | ||
| + | * [http://en.wikipedia.org/wiki/Constructing_skill_trees Constructing skill trees] | ||
| + | * [http://en.wikipedia.org/wiki/Dehaene%E2%80%93Changeux_model Dehaene–Changeux model] | ||
| + | * [http://en.wikipedia.org/wiki/Diffusion_map Diffusion map] | ||
| + | * [http://en.wikipedia.org/wiki/Dominance-based_rough_set_approach Dominance-based rough set approach] | ||
| + | * [http://en.wikipedia.org/wiki/Dynamic_time_warping Dynamic time warping] | ||
| + | * [http://en.wikipedia.org/wiki/Error-driven_learning Error-driven learning] | ||
| + | * [http://en.wikipedia.org/wiki/Evolutionary_multimodal_optimization Evolutionary multimodal optimization] | ||
| + | * [http://en.wikipedia.org/wiki/Expectation%E2%80%93maximization_algorithm Expectation–maximization algorithm] | ||
| + | * [http://en.wikipedia.org/wiki/FastICA FastICA] | ||
| + | * [http://en.wikipedia.org/wiki/Forward%E2%80%93backward_algorithm Forward–backward algorithm] | ||
| + | * [http://en.wikipedia.org/wiki/GeneRec GeneRec] | ||
| + | * [http://en.wikipedia.org/wiki/Genetic_Algorithm_for_Rule_Set_Production Genetic Algorithm for Rule Set Production] | ||
| + | * [http://en.wikipedia.org/wiki/Growing_self-organizing_map Growing self-organizing map] | ||
| + | * [http://en.wikipedia.org/wiki/HEXQ HEXQ] | ||
| + | * [http://en.wikipedia.org/wiki/Hyper_basis_function_network Hyper basis function network] | ||
| + | * [http://en.wikipedia.org/wiki/IDistance IDistance] | ||
| + | * [http://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm K-nearest neighbors algorithm] | ||
| + | * [http://en.wikipedia.org/wiki/Kernel_methods_for_vector_output Kernel methods for vector output] | ||
| + | * [http://en.wikipedia.org/wiki/Kernel_principal_component_analysis Kernel principal component analysis] | ||
| + | * [http://en.wikipedia.org/wiki/Leabra Leabra] | ||
| + | * [http://en.wikipedia.org/wiki/Linde%E2%80%93Buzo%E2%80%93Gray_algorithm Linde–Buzo–Gray algorithm] | ||
| + | * [http://en.wikipedia.org/wiki/Local_outlier_factor Local outlier factor] | ||
| + | * [http://en.wikipedia.org/wiki/Logic_learning_machine Logic learning machine] | ||
| + | * [http://en.wikipedia.org/wiki/LogitBoost LogitBoost] | ||
| + | * [http://en.wikipedia.org/wiki/Manifold_alignment Manifold alignment] | ||
| + | * [http://en.wikipedia.org/wiki/Minimum_redundancy_feature_selection Minimum redundancy feature selection] | ||
| + | * [[Mixture of experts]] | ||
| + | * [[Multiple kernel learning]] | ||
| + | * [[Non-negative matrix factorization]] | ||
| + | * [[Online machine learning]] | ||
| + | * [[Out-of-bag error]] | ||
| + | * [[Prefrontal cortex basal ganglia working memory]] | ||
| + | * [[PVLV]] | ||
| + | * [[Q-learning]] | ||
| + | * [[Quadratic unconstrained binary optimization]] | ||
| + | * [[Query-level feature]] | ||
| + | * [[Quickprop]] | ||
| + | * [[Radial basis function network]] | ||
| + | * [[Randomized weighted majority algorithm]] | ||
| + | * [[Reinforcement learning]] | ||
| + | * [[Repeated incremental pruning to produce error reduction (RIPPER)]] | ||
| + | * [[Rprop]] | ||
| + | * [[Rule-based machine learning]] | ||
| + | * [[Skill chaining]] | ||
| + | * [[Sparse PCA]] | ||
| + | * [[State–action–reward–state–action]] | ||
| + | * [[Stochastic gradient descent]] | ||
| + | * [[Structured kNN]] | ||
| + | * [[T-distributed stochastic neighbor embedding]] | ||
| + | * [[Temporal difference learning]] | ||
| + | * [[Wake-sleep algorithm]] | ||
| + | * [[Weighted majority algorithm (machine learning)]] | ||
| + | |||
| + | === Git === | ||
*[http://github.com/search?q=artificial+intelligence&ref=searchresults&type=Repositories GitHub AI] | *[http://github.com/search?q=artificial+intelligence&ref=searchresults&type=Repositories GitHub AI] | ||
*[http://github.com/search?utf8=%E2%9C%93&q=deep+learning&type= GitHub Deep Learning] | *[http://github.com/search?utf8=%E2%9C%93&q=deep+learning&type= GitHub Deep Learning] | ||
Revision as of 08:41, 20 May 2018
Wikipedia pages...
- Almeida–Pineda recurrent backpropagation
- ALOPEX
- Backpropagation
- Bootstrap aggregating
- CN2 algorithm
- Constructing skill trees
- Dehaene–Changeux model
- Diffusion map
- Dominance-based rough set approach
- Dynamic time warping
- Error-driven learning
- Evolutionary multimodal optimization
- Expectation–maximization algorithm
- FastICA
- Forward–backward algorithm
- GeneRec
- Genetic Algorithm for Rule Set Production
- Growing self-organizing map
- HEXQ
- Hyper basis function network
- IDistance
- K-nearest neighbors algorithm
- Kernel methods for vector output
- Kernel principal component analysis
- Leabra
- Linde–Buzo–Gray algorithm
- Local outlier factor
- Logic learning machine
- LogitBoost
- Manifold alignment
- Minimum redundancy feature selection
- Mixture of experts
- Multiple kernel learning
- Non-negative matrix factorization
- Online machine learning
- Out-of-bag error
- Prefrontal cortex basal ganglia working memory
- PVLV
- Q-learning
- Quadratic unconstrained binary optimization
- Query-level feature
- Quickprop
- Radial basis function network
- Randomized weighted majority algorithm
- Reinforcement learning
- Repeated incremental pruning to produce error reduction (RIPPER)
- Rprop
- Rule-based machine learning
- Skill chaining
- Sparse PCA
- State–action–reward–state–action
- Stochastic gradient descent
- Structured kNN
- T-distributed stochastic neighbor embedding
- Temporal difference learning
- Wake-sleep algorithm
- Weighted majority algorithm (machine learning)
Git
