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The number of unique labellings of the training data that can be achieved with linear classifiers is, in fact, finite. A line can bedefined by picking any pair of training points, as illustrated in [link] . Two classifiers can be defined from each such line: one that outputs a label “1” for everything on or abovethe line, and another that outputs “0” for everything on or above. There exist n 2 such pairs of training points, and these define all possible unique labellings of the training data.Therefore, there are at most 2 n 2 unique linear classifiers for any random set of n 2-dimensional features (the factor of 2 is due to the fact that for each linear classifier thereare 2 possible assignments of the labelling).

Fitting a linear classifier to 2-dimensional data. There are an infinite number of such classifiers. We can generate alinear classifier by choosing two data points, drawing a line with both points on one side, and declaring all points on or above theline to be “ + 1 ” (or “ - 1 ”) and all points below the line to be “ - 1 ” (or “ + 1 ”).
From the discussion in the previous figure, we see that the two linear classifiers depicted in this figure are equivalent for this setof data points, and hence relative to the set of n training data there are only on the order of n 2 unique linear classifiers.

Thus, instead of infinitely many linear classifiers, we realize that as far as a random sample of n training data is concerned, there are at most

2 n 2 = 2 n ! ( n - 2 ) ! 2 ! = n ( n - 1 )

unique linear classifiers. That is, using linear classification rules, there are at most n ( n - 1 ) n 2 unique label assignments for n data points. If we like, we can encode each possibility with log 2 n ( n - 1 ) 2 log 2 n bits. In d dimensions there are 2 n d hyperplane classification rules which can be encoded in roughly d log 2 n bits. Roughly speaking, the number of bits required for encoding each model is the VC dimension. Theremarkable aspect of the VC dimension is that it is often finite even when F is infinite (as in this example).

If X has d dimensions in total, we might consider linear classifiers based on 1 , 2 , , d features at a time. Lower dimensional hyperplanes are less complex than higher dimensionalones. Suppose we set

F 1 = linear classifiers using 1 feature F 2 = linear classifiers using 2 features and so on .

These spaces have increasing VC dimensions, and we can try to balance the empirical risk and a cost function depending on the VC dimension.Such procedures are often referred to as Structural Risk Minimization . This gives you a glimpse of what the VC dimension is all about. In future lectures we will revisit this topic in greaterdetail.

Hold-out methods

The basic idea of “hold-out” methods is to split the n samples D { X i , Y i } i = 1 n into a training set, D T , and a test set, D V .

D T = { X i , Y i } i = 1 m , D V = { X i , Y i } i = m + 1 n .

Now, suppose we have a collection of different model spaces { F λ } indexed by λ Λ (e.g., F λ is the set of polynomials of degree d , with λ = d ), or suppose that we have a collection of complexity penalization criteria L λ ( f ) indexed by λ ( e.g., let L λ ( f ) = R ^ ( f ) + λ c ( f ) , with λ R + ). We can obtain candidate solutions using the training set as follows. Define

R ^ m ( f ) = i = 1 m ( f ( X i ) , Y i )

and take

f ^ λ = arg min f F λ R ^ m ( f )


f ^ λ = arg min f F R ^ m ( f ) + λ c ( f ) .

This provides us with a set of candidate solutions { f ^ λ } . Then we can define the hold-out error estimate using the test set:

R ^ V ( f ) = 1 n - m + 1 i = m + 1 n ( f ( X i ) , Y i ) ,

and select the “best” model to be f ^ = f ^ λ ^ where

λ ^ = arg min λ R ^ V f ^ λ .

This type of procedure has many nice theoretical guarantees, provided both the training and test set grow with n .

Leaving-one-out cross-validation

A very popular hold-out method is the so call “leaving-one-out cross-validation” studied in depth by Grace Wahba (UW-Madison,Statistics). For each λ we compute

f ^ λ ( k ) = arg min f F 1 n i k i = 1 n ( f ( X i ) , Y i ) + λ C ( f )


f ^ λ ( k ) = arg min f F λ 1 n i k i = 1 n ( f ( X i ) , Y i ) .

Then we have cross-validation function

V ( λ ) = 1 n k = 1 n ( f λ ( k ) ( X k ) , Y k ) λ * = arg min λ V ( λ ) .


To summarize, this lecture gave a brief and incomplete survey of different methods for dealing with the issues of overfitting and modelselection. Given a set of training data, D n = { X i , Y i } i = 1 n , our overall goal is to find

f * = arg min f F R ( f )

from some collection of functions, F . Because we do not know the true distribution P X Y underlyingthe data points D n , it is difficult to get an exact handle on the risk, R ( f ) . If we only focus on minimizing the empirical risk R ^ ( f ) we end up overfitting to the training data. Two general approaches were presented.

  1. In the first approach we consider an indexed collection of spaces { F λ } λ Λ such that the complexity of F λ increases as λ increases, and
    lim λ F λ = F .
    A solution is given by
    f ^ λ * = arg min f F λ * R ^ n ( f )
    where either λ * is a function which increases with n ,
    λ * = λ ( n ) ,
    or λ * is chosen by hold-out validation.
  2. The alternative approach is to incorporate a penalty term into the risk minimization problem formulation. Here we consideran indexed collection of penalties { C λ } λ Λ satisfying the following properties:
    1. C λ : F R + ;
    2. For each f F and λ 1 < λ 2 we have C λ 1 ( f ) C λ 2 ( f ) ;
    3. There exists λ 0 Λ such that C λ 0 ( f ) = 0 for all f F .
    In this formulation we find a solution
    f ^ λ * = arg min f F R ^ n ( f ) + C λ * ( f ) ,
    where either λ * = λ ( n ) , a function growing the number of data samples n , or λ * is selected by hold-out validation.


If an estimator or classifier f ^ λ * satisfies

E R ( f ^ λ * ) inf f F R ( f ) as n ,

then we say that f ^ λ * is F -consistent with respect to the risk R . When the context is clear, we will simply say that f ^ is consistent.

Questions & Answers

where we get a research paper on Nano chemistry....?
Maira Reply
nanopartical of organic/inorganic / physical chemistry , pdf / thesis / review
what are the products of Nano chemistry?
Maira Reply
There are lots of products of nano chemistry... Like nano coatings.....carbon fiber.. And lots of others..
Even nanotechnology is pretty much all about chemistry... Its the chemistry on quantum or atomic level
no nanotechnology is also a part of physics and maths it requires angle formulas and some pressure regarding concepts
Preparation and Applications of Nanomaterial for Drug Delivery
Hafiz Reply
Application of nanotechnology in medicine
what is variations in raman spectra for nanomaterials
Jyoti Reply
ya I also want to know the raman spectra
I only see partial conversation and what's the question here!
Crow Reply
what about nanotechnology for water purification
RAW Reply
please someone correct me if I'm wrong but I think one can use nanoparticles, specially silver nanoparticles for water treatment.
yes that's correct
I think
Nasa has use it in the 60's, copper as water purification in the moon travel.
nanocopper obvius
what is the stm
Brian Reply
is there industrial application of fullrenes. What is the method to prepare fullrene on large scale.?
industrial application...? mmm I think on the medical side as drug carrier, but you should go deeper on your research, I may be wrong
How we are making nano material?
what is a peer
What is meant by 'nano scale'?
What is STMs full form?
scanning tunneling microscope
how nano science is used for hydrophobicity
Do u think that Graphene and Fullrene fiber can be used to make Air Plane body structure the lightest and strongest. Rafiq
what is differents between GO and RGO?
what is simplest way to understand the applications of nano robots used to detect the cancer affected cell of human body.? How this robot is carried to required site of body cell.? what will be the carrier material and how can be detected that correct delivery of drug is done Rafiq
analytical skills graphene is prepared to kill any type viruses .
Any one who tell me about Preparation and application of Nanomaterial for drug Delivery
what is Nano technology ?
Bob Reply
write examples of Nano molecule?
The nanotechnology is as new science, to scale nanometric
nanotechnology is the study, desing, synthesis, manipulation and application of materials and functional systems through control of matter at nanoscale
Is there any normative that regulates the use of silver nanoparticles?
Damian Reply
what king of growth are you checking .?
What fields keep nano created devices from performing or assimulating ? Magnetic fields ? Are do they assimilate ?
Stoney Reply
why we need to study biomolecules, molecular biology in nanotechnology?
Adin Reply
yes I'm doing my masters in nanotechnology, we are being studying all these domains as well..
what school?
biomolecules are e building blocks of every organics and inorganic materials.
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Source:  OpenStax, Statistical learning theory. OpenStax CNX. Apr 10, 2009 Download for free at http://cnx.org/content/col10532/1.3
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