On the Optimality of Incremental Neural Network Algorithms
Authors: Vitaly Maiorov Ron Meir
Published in: Advances in Neural Information Processing Systems 11  (NIPS 1998) (Main Conference Track), 1998

Abstract: We  study the approximation of functions by  two-layer feedforward neu(cid:173) ral  networks,  focusing  on  incremental  algorithms  which  greedily  add  units,  estimating  single  unit  parameters  at  each  stage.  As  opposed  to  standard algorithms for fixed architectures, the optimization at each stage  is  performed over a small  number of parameters, mitigating many of the  difficult numerical problems inherent in  high-dimensional non-linear op(cid:173) timization.  We  establish  upper bounds on  the  error incurred by  the  al(cid:173) gorithm, when approximating functions from the  Sobolev class, thereby  extending previous results  which only provided rates of convergence for  functions in certain convex hulls of functional spaces. By comparing our  results to recently derived lower bounds,  we  show  that the greedy algo(cid:173) rithms  are  nearly  optimal.  Combined  with  estimation  error results  for  greedy algorithms, a strong case can be made for this type of approach. 
1  Introduction and background 
A  major problem  in  the  application  of neural  networks  to  real  world  problems is  the ex(cid:173) cessively long time required for training large networks of a fixed  architecture. Moreover,  theoretical results establish the intractability of such training in  the worst case  [9][4].  Ad(cid:173) ditionally,  the problem of determining the  architecture and size of the network required to  solve  a certain  task  is  left open.  Due  to  these  problems,  several  authors  have  considered  incremental algorithms  for  constructing the  network by  the  addition  of hidden units, and  estimation of each unit's parameters incrementally.  These  approaches  possess  two desir(cid:173) able  attributes:  first,  the  optimization  is  done  step-wise,  so  that  only  a  small  number of  parameters  need  to  be  optimized at  each  stage;  and  second,  the  structure  of the  network 
-This work  was supported in part by the a grant from the  Israel  Science Foundation  tThe author was  partially supported by  the center for Absorption  in  Science, Ministry of Immi(cid:173)
grant Absorption,  State of Israel. 
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is  established concomitantly with the learning, rather than specifying it in advance.  How(cid:173) ever,  until  recently these algorithms have been rather heuristic in  nature, as  no  guaranteed  performance bounds had been established.  Note that  while there  has  been  a recent surge  of interest  in  these  types  of algorithms,  they  in  fact  date back  to  work done  in  the  early  seventies (see [3]  for a historical survey). 
The first theoretical result establishing performance bounds for incremental approximations  in  Hilbert space, was given by Jones  [8].  This work  was  later extended by Barron [2],  and  applied to  neural network approximation of functions characterized by  certain  conditions  on  their Fourier coefficients.  The  work  of Barron  has  been  extended  in  two  main direc(cid:173) tions.  First, Lee et at.  [10]  have considered approximating general functions using Hilbert  space  techniques,  while  Donahue et al.  [7]  have  provided powerful  extensions of Jones'  and  Barron's results  to general  Banach spaces.  One of the  most impressive results of the  latter work is the demonstration that iterative algorithms can, in many cases, achieve nearly  optimal rates of convergence, when approximating convex hulls. 
While this paper is concerned mainly with issues of approximation, we  comment that it is  highly relevant to  the statistical  problem  of learning  from  data in  neural  networks.  First,  Lee et at.  [10]  give estimation error bounds  for algorithms performing incremental  opti(cid:173) mization  with  respect to  the  training  error.  Under certain  regularity  conditions,  they  are  able to achieve rates of convergence comparable to those obtained by the much more com(cid:173) putationally  demanding algorithm  of empirical  error minimization.  Moreover,  it  is  well  known  that  upper bounds  on  the  approximation  error are  needed  in  order to  obtain  per(cid:173) formance  bounds,  both  for  parametric  and  nonparametric estimation,  where  the  latter  is  achieved using  the  method of complexity regularization.  Finally,  as  pointed out by  Don(cid:173) ahue et al.  [7],  lower bounds on  the approximation error are crucial  in  establishing  worst  case speed limitations for learning. 
The main contribution of this paper is  as follows.  For functions belonging to  the Sobolev  class (see definition below), we establish, under appropriate conditions, near-optimal rates  of convergence for the incremental approach, and obtain explicit bounds on the parameter  values of the network.  The latter bounds are often crucial for establishing estimation error  rates.  In  contrast  to  the  work  in  [10]  and  [7],  we  characterize  approximation  rates  for  functions belonging to standard smoothness classes, such as  the Sobolev class.  The former  work establishes rates  of convergence with  respect to  the convex  hulls  of certain  subsets  of functions,  which do not relate in  a any  simple way to  standard functional classes (such  as  Lipschitz, Sobolev,  Holder, etc.).  As  far  as  we  are  aware,  the results reported here are  the  first  to  report on  such bounds for  incremental  neural  network procedures.  A detailed  version of this  work, complete with the detailed proofs, is available in  [13]. 
2  Problem statement 
We  make use  of the  nomenclature and definitions  from  [7].  Let H  be  a Banach  space of  functions  with norm  II  . II.  For concreteness we  assume henceforth that the  norm is  given  by  the  Lq  norm,  1 < q  < 00, denoted by II  . Ilq. Let linn H  consist of all  sums of the form  L~=l aigi , gi  E H  and arbitrary ai, and COn H  is  the set of such sums  with ai  E  [0,1]  and  L~=l ai = 1.  The distances, measured in  the  Lq  norm, from  a function f  are given by 
dist(1innH,f) =  inf {l lh - fllq  :  hE linnH},  dist(conH , f) =  inf {l lh - fllq  :  hE conH}. 
The linear span of H  is  given by  linH  = Un linn H,  while  the convex-hull of H  is  coH =  Uncon H. We  follow standard notation and denote closures of sets by a bar, e.g.  coH is  the  closure of the convex hull of H. In this work we focus on the special case where 
H  =  H1}  ~ {g  : g(x)  =  eCJ(aT x + b),  lei::;  1}, IICJ(·)llq  ::;  I},