# Statistical Mechanics of On-Line Learning Under Concept Drift

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## Abstract

**:**

## 1. Introduction

#### 1.1. Concept Drift and Continual Learning

#### 1.2. Models of On-Line Learning Under Concept Drift

#### 1.3. Relation to Earlier Work

#### 1.4. Outline

## 2. Models and Mathematical Analysis

#### 2.1. Learning Vector Quantization

#### 2.1.1. Nearest Prototype Classification and Winner-Takes-All Training

#### 2.1.2. Clustered Model Data

#### 2.2. Soft Committee Machines

#### 2.2.1. Network Definition

#### 2.2.2. Regression Scheme and On-Line Gradient Descent

#### 2.2.3. Student–Teacher Scenario and Model Data

#### 2.3. The Dynamics of On-Line Training in Stationary Environments

- (a)
- Order parameters

- (b)
- Recursions

- (c)
- Averages over the Model Data

- (d)
- Self-Averaging Properties

- (e)
- Continuous Time Limit and ODE

- (f)
- Generalization error

- (g)
- Learning curves

#### 2.4. The Learning Dynamics Under Concept Drift

#### 2.4.1. Virtual Drift

#### 2.4.2. Real Drift

#### 2.4.3. Weight Decay

## 3. Results and Discussion

#### 3.1. Learning Vector Quantization in the Presence of Real Concept Drift

#### 3.2. SCM Regression in the Presence of Real Concept Drift

## 4. Conclusions

#### 4.1. Brief Summary

#### 4.2. Future Work and Extensions

- The systematic investigation of virtual drifts as in, for instance, non-stationary label noise, prior weights ${p}_{1,2}$ or cluster separation $\lambda $ is readily possible by consideration of explicitly time-dependent ODE.
- The restriction to LVQ systems with one prototype per class results, effectively, in the parameterization of linear class boundaries only. This limitation can be lifted by considering distances different from the simple Euclidean measure (see, e.g., [29]). Alternatively, systems with several prototypes per class correspond to non-linear (piece-wise linear) decision boundaries which has non-trivial effects on the training dynamics, as demonstrated for stationary environments in [49].
- Similarly, the investigation of SCM student–teacher scenarios with more general settings of K and M will provide insight into the interplay of concept drift with the larger number of possible plateau states for $K,M>2$. Over- and under-fitting effects in mismatched situations with $K\ne M$ will be in the center of interest.
- The shallow SCM architectures studied here are limited to a single hidden layer of units. The important extension to deeper networks with several hidden layers will be addressed in forthcoming studies.
- It will be interesting to explore the extent to which the theoretically studied phenomena can be observed in practical situations. To this end, we will investigate the behavior of LVQ and SCM in realistic training set-ups with real world data streams.

#### 4.3. Perspectives and Challenges

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## Abbreviations

CLT | Central Limit Theorem |

LVQ | Learning Vector Quantization |

NPC | Nearest Prototype Classification |

ODE | Ordinary Differential Equations |

r.h.s. | right hand side |

SCM | Soft Committee Machine |

WTA | Winner Takes All |

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**Figure 1.**Clustered Model Density. Illustration of the clustered density, Equation (4), in $N=200$ dimensions, here with ${p}_{1}=0.4,{p}_{2}=0.6$ and ${v}_{1}=0.64,{v}_{2}=1.44$. Triangles (squares) represent $120\phantom{\rule{0.166667em}{0ex}}(180)$ vectors $\overrightarrow{\xi}$ from the clusters centered at $\lambda {\overrightarrow{B}}_{1}$ ($\lambda {\overrightarrow{B}}_{2}$) with $\lambda =1.5$, respectively. (

**a**) Projections ${\overrightarrow{B}}_{1,2}\xb7\overrightarrow{\xi}$ of the data. The cluster centers are marked by larger symbols. (

**b**) Projections ${\overrightarrow{w}}_{1,2}\xb7\overrightarrow{\xi}$ on two randomly chosen orthonormal vectors ${\overrightarrow{w}}_{1,2}$.

**Figure 2.**LVQ under Concept Drift: Learning Curves and the Role of the Learning Rate. LVQ1 training from data according to the model density (Equation (4)) with $\lambda =1,{p}_{1}={p}_{2}=0.5$ and ${v}_{1}={v}_{2}=0.5$ in the presence of real concept drift. (

**a**) Learning curves ${\u03f5}_{g}(\alpha )$ for $\delta =1$ and various learning rates $\eta $. Symbols and error bars mark the mean results and standard deviations observed in 25 randomized simulations for $N=1000$ with $\eta =1$ as an example. (

**b**) Asymptotic $(\alpha \to \infty )$ generalization error as a function of the learning rate $\eta $ for different drift parameters $\delta $ and in the stationary environment with $\delta =0$.

**Figure 3.**LVQ under Concept Drift: Asympotic Generalization and the Influence of Weight Decay. LVQ1 in the presence of a real drift with model parameters $\lambda =1,{v}_{1}={v}_{2}=0.5,{p}_{1}={p}_{2}=0.5$. (

**a**) The $(\alpha \to \infty )$ asymptotic generalization error of LVQ1 as obtained with an optimized constant learning rate. Empty circles correspond to numerical results for different drift parameters, the filled circle represents stationary data, for which ${\u03f5}_{g}^{\infty}(\delta \phantom{\rule{-0.166667em}{0ex}}=\phantom{\rule{-0.166667em}{0ex}}0)\approx 0.158$. The dashed line corresponds to a fit of the form ${\u03f5}_{g}^{\infty}(\delta \phantom{\rule{-0.166667em}{0ex}}=\phantom{\rule{-0.166667em}{0ex}}0)+0.166\phantom{\rule{0.166667em}{0ex}}{\delta}^{1/2}$. (

**b**) Learning curves in the model with learning rate $\eta =2.0$ and drift parameter $\delta =1.0$. The three curves correspond to learning without weight decay (upper, solid line), with $\gamma =2$ (lower, dash-dotted line) and $\gamma =5$ (middle, dashed line).

**Figure 4.**Regression under Concept Drift: Learning Curves. Gradient-based training of the Soft Committee Machine with $K\phantom{\rule{-0.166667em}{0ex}}=\phantom{\rule{-0.166667em}{0ex}}M\phantom{\rule{-0.166667em}{0ex}}=\phantom{\rule{-0.166667em}{0ex}}2$ and orthogonal teacher vectors in the presence of real target drift, with learning rate $\eta =0.5$ and initial conditions as specified in Equation (35). (

**a**) Learning curves for the stationary case with $\delta =0$ (lower line), for weak drift with $\delta =0.005$ (middle) and for strong drift with $\delta =0.03$ (upper line). Symbols represent the result of single Monte Carlo simulation runs for system size $N=500$. (

**b**) The corresponding evolution of the student–teacher overlaps ${R}_{11}={R}_{22}$ and ${R}_{12}={R}_{21}$ vs. $\alpha $ for the stationary case with $\delta =0$ (lower and upper lines), for weak drift with $\delta =0.005$ (intermediate) and strong drift with $\delta =0.03$ (center, all overlaps equal).

**Figure 5.**Regression under Concept Drift: Plateaus and Specialized States. Soft Committee Machine, regression in the presence of real target drift, learning rate and model parameters as in Figure 4. (

**a**) The generalization error vs. the drift parameter $\delta $ for $\gamma =0$, in the symmetric plateau state with ${R}_{11}={R}_{22}$ and ${R}_{12}={R}_{21}$ (dashed line) and in the $\alpha \to \infty $ stationary state (solid). (

**b**) The influence of weight decay: For a given drift with $\delta =0.015$, the $\alpha \to \infty $ asymptotic generalization error is displayed as a function of the weight decay parameter $\gamma $. In addition, the dashed line marks ${\u03f5}_{g}$ in the unspecialized plateau state.

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**MDPI and ACS Style**

Straat, M.; Abadi, F.; Göpfert, C.; Hammer, B.; Biehl, M.
Statistical Mechanics of On-Line Learning Under Concept Drift. *Entropy* **2018**, *20*, 775.
https://doi.org/10.3390/e20100775

**AMA Style**

Straat M, Abadi F, Göpfert C, Hammer B, Biehl M.
Statistical Mechanics of On-Line Learning Under Concept Drift. *Entropy*. 2018; 20(10):775.
https://doi.org/10.3390/e20100775

**Chicago/Turabian Style**

Straat, Michiel, Fthi Abadi, Christina Göpfert, Barbara Hammer, and Michael Biehl.
2018. "Statistical Mechanics of On-Line Learning Under Concept Drift" *Entropy* 20, no. 10: 775.
https://doi.org/10.3390/e20100775