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# System prepended metadata

title: Untitled

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## Structuur 
1) Abstract
2) Inleiding
    - Overzicht welke technieken
    - Belangrijke begrippen
3) Problemen en challenges over incremental ML
4) Per techniek hoofdstuk
    - Uitleg techniek
    - Voordelen van deze techniek
    - Welke problemen oplost
    - Kort resultaten bespreken
    - (Researchers in dit gebied)
5) Vergelijkingen technieken
    - Resultaten bespreken
    - Overzicht welke techniek voor welk probleem
6) Conclusie
7) Bronvermelding

## Technieken
- Deep neural network
- Spiking Neural Networks
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- Kernel-based online machine learning
- Machine learning: Stream learning 
    - StreamLearner architecture (not sure of da een standaard is)
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- Ensembles
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- Streaming autoencoder
- Autoencoder
- Kolmogorov-Smirnov test
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- Online variational inference for multivariate Gaussians
- VIGO
- Online Bayesian Classifier
- Support vector machine
- Decision trees
- Random forests
- bayes trees
- Biasis correction methods
- learn++
- ARTMPA(neural network architecture)
- RTDP (real time dynamic progamming)
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- Forgetting function (1993 -> still used alot)
- Discard after learn
- Stream Chunk incremental learning (scil)
- Incremental learning in general
- Batch-incremental
- Instance-incremental
- Overview 
- Convolutional Neural Network
- IPCA
- Adain
- ILUGA
- VFDT
- Lambda Learner
- Graph Neural Networks
- Reinforcement learning
## Challenges
- Scaling to more data
- Class imbalance
- Computation time
- Anomaly detection
- Concept drift
- Robustness for noise
- Class evolution
- Scalability
- Fast adaptation
- Generalization
- Noisy data
- Non stationary data
- Neuron merging
- Catastrophic forgetting
- Delayed labels
- Temporal dependence
- Model pruning
- Preprocessing
- Feature learning
## Takenverdeling
Stef: 
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Brecht:
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Christophe:
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Thomas:


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