學習第n個任務會比以前的容易嗎？

Sebastian Thrun

Abstract

This paper investigates learning in a lifelong context. Lifelong learning addresses situations in which a learner faces a whole stream of learning tasks. Such scenarios provide the opportunity to transfer knowledge across multiple learning tasks, in order to generalize more accurately from less training data. In this paper, several different approaches to lifelong learning are described and applied in an object recognition domain. It is shown that across the board, lifelong learning approaches generalize consistently more accurately from less training data, by their ability to transfer knowledge across learning tasks.

Introduction

人在學習過程當中並不僅使用提供的訓練數據，而是會綜合過往的經驗。就像學車的時候，也許你才學了幾天，可是你從學就學會了識別路牌，有一些基本的機械知識，這些都會幫助你學習駕駛。

lifelong learning架構是指，假設你面對的任務是你整我的生中全部任務，這些任務間的學習是能夠相互促進的。從前面學習的任務中提取經驗會有利於新的任務的學習。

咱們能夠假設每一個新的任務是一個concept，每一個concept對應一個函數f。因此遇到一個任務，咱們須要先知道它屬於哪一個concept，用哪一個函數。咱們在學習第n個任務的時候，前n-1個任務的數據也會有用，這些數據叫作支持集。

Memory-Based Learning Approaches

基於記憶的方式。

KNN 和 Shepard

Shepard是給KNN每一個點加了權重，距離越遠，權重越小。

學習新的表徵

咱們認爲一個好的表徵是讓同類的樣本間距離近，不一樣類的樣本間距離遠。

學習距離函數

能夠用神經網絡來學習距離函數，設定一個閾值，來判斷樣本是屬於哪一個concept。

基於神經網絡的方式

反向傳播

Learning with Hints

看起來像multi-task leanring的原始版本。

EBNN

做者以前的一篇論文。EBNN估計目標函數的斜率（tangents）。使用了Tangent-Prop算法。

實驗結果

ENBB最好，有知識遷移效果。

Discussion

Learning becomes easier when embedded in a lifelong learning context.

Y. S. Abu-Mostafa. Learning from hints in neural networks. Journal of Complexity, 6: 192-198,

W-K. Ahn and W F. Brewer. Psychological studies of explanation-based learning. In
G. Dejong, editor, Investigating Explanation-Based Learning. Kluwer Academic Publishers,
BostonlDordrechtILondon, 1993.

T. M. Mitchell and S. Thrun. Explanation-based neural network learning for robot control. In
S. J. Hanson, J. Cowan, and C. L. Giles, editors, Advances in Neural Information Processing
Systems 5, pages 287-294, San Mateo, CA, 1993. Morgan Kaufmann.

] J. O'Sullivan, T. M. Mitchell, and S. Thrun. Explanation-based neural network learning from
mobile robot perception. In K. Ikeuchi and M. Veloso, editors, Symbolic Visual Learning. Oxford
University Press, 1995.

D. E. Rumelhart, G. E. Hinton, and R. J. Williams. Learning internal representations by error
propagation. In D. E. Rumelhart and 1. L. McClelland, editors, Parallel Distributed Processing.
Vol. I + II. MIT Press, 1986.

S. Thrun. Explanation-Based Neural Network Learning: A Lifelong Learning Approach. KluwerAcademic Publishers, Boston, MA, 1996. to appear.