computer science Computer science is the study of computation, automation, and information. Computer science spans theoretical disciplines (such as algorithms, theory of computation, information theory, and automation) to Applied science, practical discipli ...

, incremental learning is a method of

machine learning Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence. Machine ...

in which input data is continuously used to extend the existing model's knowledge i.e. to further train the model. It represents a dynamic technique of

supervised learning Supervised learning (SL) is a machine learning paradigm for problems where the available data consists of labelled examples, meaning that each data point contains features (covariates) and an associated label. The goal of supervised learning alg ...

and

unsupervised learning Unsupervised learning is a type of algorithm that learns patterns from untagged data. The hope is that through mimicry, which is an important mode of learning in people, the machine is forced to build a concise representation of its world and t ...

that can be applied when training data becomes available gradually over time or its size is out of system memory limits. Algorithms that can facilitate incremental learning are known as incremental machine learning algorithms. Many traditional machine learning algorithms inherently support incremental learning. Other algorithms can be adapted to facilitate incremental learning. Examples of incremental algorithms include

decision tree A decision tree is a decision support tool that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm that only contains condit ...

s (IDE4, ID5R an
gaenari
,

decision rules A decision tree is a decision support tool that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm that only contains condit ...

artificial neural network Artificial neural networks (ANNs), usually simply called neural networks (NNs) or neural nets, are computing systems inspired by the biological neural networks that constitute animal brains. An ANN is based on a collection of connected unit ...

s ( RBF networks, Learn++, Fuzzy ARTMAP, TopoART,Marko Tscherepanow, Marco Kortkamp, and Marc Kammer
A Hierarchical ART Network for the Stable Incremental Learning of Topological Structures and Associations from Noisy Data
Neural Networks, 24(8): 906-916, 2011 and IGNG) or the incremental SVM.Diehl, Christopher P., and Gert Cauwenberghs
SVM incremental learning, adaptation and optimization
Neural Networks, 2003. Proceedings of the International Joint Conference on. Vol. 4. IEEE, 2003. The aim of incremental learning is for the learning model to adapt to new data without forgetting its existing knowledge. Some incremental learners have built-in some parameter or assumption that controls the relevancy of old data, while others, called stable incremental machine learning algorithms, learn representations of the training data that are not even partially forgotten over time. Fuzzy ARTCarpenter, G.A., Grossberg, S., & Rosen, D.B.
Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system
Neural Networks, 4(6): 759-771, 1991 and TopoART are two examples for this second approach. Incremental algorithms are frequently applied to

data stream In connection-oriented communication, a data stream is the transmission of a sequence of digitally encoded coherent signals to convey information. Typically, the transmitted symbols are grouped into a series of packets. Data streaming has bec ...

s or

big data Though used sometimes loosely partly because of a lack of formal definition, the interpretation that seems to best describe Big data is the one associated with large body of information that we could not comprehend when used only in smaller am ...

, addressing issues in data availability and resource scarcity respectively. Stock trend prediction and user profiling are some examples of data streams where new data becomes continuously available. Applying incremental learning to big data aims to produce faster

classification Classification is a process related to categorization, the process in which ideas and objects are recognized, differentiated and understood. Classification is the grouping of related facts into classes. It may also refer to: Business, organizat ...

forecasting Forecasting is the process of making predictions based on past and present data. Later these can be compared (resolved) against what happens. For example, a company might estimate their revenue in the next year, then compare it against the actual ...

times.

References

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External links

*Brief Introduction to Streaming data and Incremental Algorithm

*Incremental learning algorithms and application
LibTopoART: A software library for incremental learning tasks

Creme: Library for incremental learning
* gaenari
C++ incremental decision tree algorithm
* youtube search result
Incremental Learning
Machine learning algorithms