IPC Classification

Class code (prefix) Descriptions Number of results
G06N 3/00 Computing arrangements based on biological models
G06N 3/02 Neural networks
G06N 3/004 Artificial life, i.e. computing arrangements simulating life
G06N 3/006 Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
G06N 3/008 Artificial life, i.e. computing arrangements simulating life based on physical entities controlled by simulated intelligence so as to replicate intelligent life forms, e.g. based on robots replicating pets or humans in their appearance or behaviour
G06N 3/09 Supervised learning
G06N 3/10 Interfaces, programming languages or software development kits, e.g. for simulating neural networks
G06N 3/12 Computing arrangements based on biological models using genetic models
G06N 3/042 Knowledge-based neural networks; Logical representations of neural networks
G06N 3/043 Architecture, e.g. interconnection topology based on fuzzy logic, fuzzy membership or fuzzy inference, e.g. adaptive neuro-fuzzy inference systems [ANFIS]
G06N 3/044 Recurrent networks, e.g. Hopfield networks
G06N 3/045 Combinations of networks
G06N 3/047 Probabilistic or stochastic networks
G06N 3/048 Activation functions
G06N 3/049 Temporal neural networks, e.g. delay elements, oscillating neurons or pulsed inputs
G06N 3/063 Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
G06N 3/065 Analogue means
G06N 3/067 Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using optical means
G06N 3/082 Learning methods modifying the architecture, e.g. adding, deleting or silencing nodes or connections
G06N 3/084 Backpropagation, e.g. using gradient descent
G06N 3/086 Learning methods using evolutionary algorithms, e.g. genetic algorithms or genetic programming
G06N 3/088 Non-supervised learning, e.g. competitive learning
G06N 3/091 Active learning
G06N 3/092 Reinforcement learning
G06N 3/094 Adversarial learning
G06N 3/096 Transfer learning
G06N 3/098 Distributed learning, e.g. federated learning
G06N 3/123 DNA computing
G06N 3/126 Evolutionary algorithms, e.g. genetic algorithms or genetic programming
G06N 3/0442 Recurrent networks, e.g. Hopfield networks characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
G06N 3/0455 Auto-encoder networks; Encoder-decoder networks
G06N 3/0464 Convolutional networks [CNN, ConvNet]
G06N 3/0475 Generative networks
G06N 3/0495 Quantised networks; Sparse networks; Compressed networks
G06N 3/0499 Feedforward networks
G06N 3/0895 Weakly supervised learning, e.g. semi-supervised or self-supervised learning
G06N 3/0985 Hyperparameter optimisation; Meta-learning; Learning-to-learn
G06N 5/00 Computing arrangements using knowledge-based models
G06N 5/01 Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
G06N 5/02 Knowledge representation; Symbolic representation
G06N 5/04 Inference or reasoning models
G06N 5/022 Knowledge engineering; Knowledge acquisition
G06N 5/025 Extracting rules from data
G06N 5/043 Distributed expert systems; Blackboards
G06N 5/045 Explanation of inference; Explainable artificial intelligence [XAI]; Interpretable artificial intelligence
G06N 5/046 Forward inferencing; Production systems
G06N 5/047 Pattern matching networks; Rete networks
G06N 5/048 Fuzzy inferencing
G06N 7/00 Computing arrangements based on specific mathematical models
G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
G06N 7/02 Computing arrangements based on specific mathematical models using fuzzy logic
G06N 7/04 Physical realisation
G06N 7/06 Simulation on general purpose computers
G06N 7/08 Computing arrangements based on specific mathematical models using chaos models or non-linear system models
G06N 10/00 Quantum computing, i.e. information processing based on quantum-mechanical phenomena
G06N 10/20 Models of quantum computing, e.g. quantum circuits or universal quantum computers
G06N 10/40 Physical realisations or architectures of quantum processors or components for manipulating qubits, e.g. qubit coupling or qubit control
G06N 10/60 Quantum algorithms, e.g. based on quantum optimisation, or quantum Fourier or Hadamard transforms
G06N 10/70 Quantum error correction, detection or prevention, e.g. surface codes or magic state distillation
G06N 10/80 Quantum programming, e.g. interfaces, languages or software-development kits for creating or handling programs capable of running on quantum computers; Platforms for simulating or accessing quantum computers, e.g. cloud-based quantum computing
G06N 20/00 Machine learning
G06N 20/10 Machine learning using kernel methods, e.g. support vector machines [SVM]
G06N 20/20 Ensemble learning
G06N 99/00 Subject matter not provided for in other groups of this subclass