Chapter 6 The Structural Risk Minimization Principle Junping Zhang jpzhang@fudan.edu.cn Intelligent...

Chapter 6 The Structural Risk Minimization Principle

Junping Zhangjpzhang@fudan.edu.cn

Intelligent Information Processing Laboratory, Fudan UniversityMarch 23, 2004

Objectives

Structural risk minimization

Two other induction principles

The Scheme of the SRM induction principle

Real-Valued functions

Principle of SRM

SRM

Minimum Description Length and SRM inductive principles

The idea about the Nature of Random Phenomena

Minimum Description Length Principle for the Pattern Recognition Problem

Bounds for the MDL SRM for the simplest Model and MDL The Shortcoming of the MDL

The idea about the Nature of Random Phenomena

Probability theory (1930s, Kolmogrov) Formal inference Axiomatization hasn’t considered nat

ure of randomness Axioms: given probability measures

The idea about the Nature of Random Phenomena

The model of randomness Solomonoff (1965), Kolmogrov (1965), Ch

aitin (1966). Algorithm (descriptive) complexity

The length of the shortest binary computer program

Up to an additive constant does not depend on the type of computer.

Universal characteristic of the object.

A relatively large string describing an object is random If algorithm complexity of an object is high If the given description of an object cannot be

compressed significantly. MML (Wallace and Boulton, 1968)& MDL

(Rissanen, 1978) Algorithm Complexity as a main tool of induc

tion inference of learning machines

Minimum Description Length Principle for the Pattern Recognition Problem

Given l pairs containing the vector x and the binary value ω

Consider two strings: the binary string

Question

Q: Given (147), is the string (146) a random object?

A: to analyze the complexity of the string (146) in the spirit of Solomonoff-Kolmogorov-Chaitin ideas

Compress its description

Since ω i i=1,…l are binary values, the string (146) is described by l bits.

Since training pairs were drawn randomly and independently.

The value ω i depend on the vector xi but not on the vector xj.

Model

General Case: not contain the perfect table.

Randomness

Bounds for the MDL

Q: Does the compression coefficient

K(T) determine the probability of the test error in classification (decoding) vectors x by the table T?

A: Yes

Comparison between the MDL and ERM in the simplest model

SRM for the simplest Model and MDL

SRM for the simplest Model and MDL

The power of compression coefficient

To obtain bound for the probability of error

Only information about the coefficient need to be known.

The power of compression coefficient

How many examples we used How the structure of code books was

organized Which code book was used and how

many tables were in this code book. How many errors were made by the

table from the code book we used.

MDL principle

To minimize the probability of error One has to minimize the coefficient

of compression

The shortcoming of the MDL

MDL uses code books with a finite number of tables.

Continuously depends on parameters, one has to first quantize that set to make the tables.

Quantization

How do we make the ‘smart’ quantization for a given number of observations.

For a given set of functions, how can we construct a code book with a small number of tables but with good approximation ability?

The shortcoming of the MDL

Finding a good quantization is extremely difficult and determines the main shortcoming of MDL principle.

The MDL principle works well when the problem of constructing reasonable code books has a good solution.

Consistency of the SRM principle and asymptotic bounds on the rate of convergence

Q: Is the SRM consistent? What is the bound on the

(asymptotic) rate of convergence?

Consistency of the SRM principle.

Simplification version

Remark

To avoid choosing the minimum of functional (156) over the infinite number of elements of the structure.

Additional constraint Choose the minimum from the first l

elements of the structure where l is equal to the number of observations.

Discussions and Example

The rate of convergence is determined by two contradictory requirements on the rule n=n(l). The first summand: The larger n=n(l) , the small

er is the deviation The second summand: The larger n=n(l), the lar

ger deviation For structures with a known bound on the r

ate of approximation, select the rule that assures the largest rate of convergence.

Bounds for the regression estimation problem

The model of regression estimation by series expansion

Example

The problem of approximating functions

To get high asymptotic rate of approximation

the only constraint is that the kernel should be a bounded

function which can be described as a family of functions possessing finite VC dimension.

Problem of local risk minimization

Local Risk Minimization Model

Note

Using local risk minimization methods, one probably does not need rich sets of approximating functions. Whereas the classical semi-local

methods are based on using a set of constant functions.

Note

For local estimation functions in the one-dimensional case, it is probably enough to consider elements Sk, k=0,1,2,3 containing the polynomials of degree 0,1,2,3

Summary MDL SRM Local Risk Functional