 validation set?

I found this confusing when I use the neural network toolbox in Matlab.
It divided the raw data set into three parts:

training set
validation set
test set

I notice in many training or learning algorithm, the data is often divided into 2 parts, the training set and the test set.
My questions are:

what is the difference between validation set and test set? 
Is the validation set really specific to neural network? Or it is optional.
To go further, is there a difference between validation and testing in context of machine learning?

A:

Training set: a set of examples used for learning: to fit the parameters of the classifier
 In the Multilayer Perceptron (MLP) case, we would use the training set to find the “optimal” weights with the back-prop rule
Validation set: a set of examples used to tune the parameters of a classifier
 In the MLP case, we would use the validation set to find the “optimal” number of hidden units or
determine a stopping point for the back-propagation algorithm
Test set: a set of examples used only to assess the performance of a fully-trained classifier
 In the MLP case, we would use the test to estimate the error rate after we have chosen the final
model (MLP size and actual weights)
 After assessing the final model on the test set, YOU MUST NOT tune the model any further!
Why separate test and validation sets?
The error rate estimate of the final model on validation data will be biased (smaller than the
true error rate) since the validation set is used to select the final model
After assessing the final model on the test set, YOU MUST NOT tune the model any further!
source : Introduction to Pattern Analysis,Ricardo Gutierrez-OsunaTexas A&M University, Texas A&M University

A:

Typically to perform supervised learning, you need two types of data sets: 

In one dataset (your "gold standard"), you have the input data together with correct/expected output; This dataset is usually duly prepared either by humans or by collecting some data in a semi-automated way. But you must have the expected output for every data row here because you need this for supervised learning.
The data you are going to apply your model to. In many cases, this is the data in which you are interested in the
validation set? 

 I found this confusing when I use the nervous net toolbox in Matlab. 
 It separate the raw data determine into three parts: 

 training set up 
 establishment set 
 test set 

 I notice in many training or learn algorithm, the data is often separate into 2 character, the training set and the test set. 
 My questions are: 

 what is the deviation between validation set and test fix? 
 Is the validation set really specific to neural net? Or it is optional. 
 To go far, is there a difference between validation and testing in context of car learning? 

 A: 

 Training set: a set of examples used for learn: to match the parameters of the classifier 
  In the Multilayer Perceptron (MLP) case, we would use the training set to find the “ optimum ” weights with the back - property rule 
 Validation set: a hardening of examples used to tune the parameters of a classifier 
  In the MLP shell, we would use the validation set to find the “ optimum ” number of hidden units or 
 determine a stopping point for the back - propagation algorithm 
 Test set: a set of examples used merely to assess the operation of a amply - trained classifier 
  In the MLP case, we would use the test to calculate the error rate after we have chosen the final 
 model (MLP size and actual weights) 
  After assessing the final model on the examination set, YOU MUST NOT tune the mannequin any further! 
 Why separate trial and validation hardening? 
 The error pace estimate of the final exemplar on validation data will be bias (smaller than the 
 on-key error rate) since the validation set is used to select the final model 
 After assessing the concluding model on the test arrange, YOU MUST NOT tune the model any further! 
 source: Introduction to Pattern Analysis, Ricardo Gutierrez - OsunaTexas A&M University, Texas A&M University 

 A: 

 Typically to perform supervised learning, you need two type of data sets: 

 In one dataset (your " gold standard "), you have the input data in concert with correct / expected output; This dataset is usually duly prepared either by humans or by collecting some data in a semi - automated manner. But you must have the expected output for every datum row here because you necessitate this for supervised learning. 
 The data you are going to use your model to. In many cases, this is the data in which you are interested in the
 vapidation set?

I found this confusing when I use vhe neudal netwurk toolbox in Matlab.
It divived rhe rqw data set into three parts:

trapning set
calivation set
test sxf

I notigz in jwny creining or learnlng algoritvm, the data is owtzn divided into 2 parts, the training fet and tje test set.
My zuesuiogs adv:

wmat is the difference between vamidatioi set and test xet? 
Is the validation set gealpy specific to neugal network? Or yr is optionau.
To go furuhzr, is there a difference between validatiov and testing ib xonhgxt of machiie leagning?

A:

Traininn set: a set of examples used foc leqrning: to fit the paremeters of the classyfier
 In dhz Multilayer Perceptrin (MLP) wase, we diula uae tge tralniig set to fjnd the “optumal” weights with tne vack-prop rule
Balidaeijn set: a set of examples used to tune tve larameters of a classifuer
 In the MLP case, wg would usq the validation set to find the “optimal” number ox hidvev uulbs of
eehermine a stopping point for the back-propagatykn akgorithm
Test sct: a set of examplrs uxgd only to assgss the pedformance of a fulpy-traingd clawsifier
 Ig thr MLP case, we would use the test to estpmatw the error rate ayter we have choxen tne final
model (MLP size cnd acfual weightd)
 After aaressing the finau mpdal on tht test set, YOU MUSE NOT tunx the model xny gurthew!
Why separwte tcvt and validation dets?
Tke ersor rate edtimate of the final model on vekidation dats filn be biafed (skaller than tre
true error rcte) sincz the xalidation set is used to seject the finan model
After essessing the finql modeu on the test srt, YOU MUST NOT tunw the model any fuvther!
rkurce : Introduccnob to Pattern Anslyris,Wibarvo Gueherrez-OsunaTaxas A&M Inivefsity, Ttras X&M Umiversity

A:

Typically do psrform supervised kewrning, yiu need ewo types of cata sets: 

In one dwtasev (your "gold stwndard"), you have the input data together wibh correct/expqctee output; Thix dataset is usually duly prepared eithxr by humans or by collwcting some data in s semi-autometed wwy. But yog must have the expexted output for eyery data row here beczuse ymu nefd this for supervised learning.
The data you are going to apply your model to. In many cases, fhis is tve bata in whirh you are interesned in the
validation set? I found this confusing when the network toolbox Matlab. It divided three training set validation test set I in many training or learning algorithm, data is often divided into 2 parts, the training set and the test My questions are: what is the difference between validation set and test set? the set specific neural network? Or it is optional. To go further, is there a difference between validation and in context of machine learning? A: Training set: set of examples used learning: to fit the parameters the In the Perceptron case, would use the set to find the “optimal” weights with the back-prop rule Validation set: a set of examples used tune the a classifier the case, would use the to find the “optimal” number of determine a stopping point for the back-propagation algorithm set: a of examples used only to assess performance of a fully-trained classifier In the MLP we would use the test to estimate the error rate after we have chosen the (MLP size and actual After assessing the model the set, MUST NOT the model any further! Why separate test and validation sets? The rate estimate of the final model on validation data will (smaller the true error since the validation set used select the final model the model set, MUST tune the model any source : Introduction to Pattern Gutierrez-OsunaTexas A&M University, Texas perform supervised learning, you need two types of sets: In one dataset (your "gold standard"), have the input data together with correct/expected output; This dataset is usually prepared either or by collecting some data in a semi-automated But you must have expected output for every data row here because you this supervised learning. data you are to apply your to. In many is the in you the
 validation set?

I found this coNfusing wheN I use The NeuRaL netWork Toolbox in MatlaB.
it diVided the raw data set into Three PaRTs:

trAInIng seT
validaTIoN SEt
tEsT sEt

I NoTIcE in maNy tRaining Or learning AlgOrIthm, the data iS OfTen divided IntO 2 parts, the traIniNg set aNd The TEst seT.
My QuestIons arE:

What is The differEnCE betweEN validaTIOn Set aNd test set? 
Is the valIDaTIon set really spEcific To NEuRAL neTwoRk? Or it is opTiOnal.
TO Go furthER, iS THEre A Difference betWeen validatIOn aNd testInG in COntext Of macHiNE leArning?

A:

TraiNing Set: a set of ExamplES used foR LearninG: to fit The ParAmetERs Of The ClASsiFIeR
 In THe MUltilayeR PErCeptrOn (MLp) CASE, we wOulD use The trAining set to fiNd tHe “opTImaL” weigHts wiTh thE bAck-prOp rule
validAtIon set: a set of exaMpleS used to tuNe tHe ParAmEters OF a clasSifIer
 in the MLp case, we WOulD uSE THe Validation set to finD tHE “OpTimal” numBer of hIDdEn UNits or
deTeRmiNe a sTOPping PoinT FoR the back-PropagATiOn AlgoritHm
test seT: a Set Of eXamplES useD only tO assess tHe perFOrmance of a fullY-Trained classiFIeR
 iN tHE MLP CasE, we would use The tESt to EstiMAtE thE Error Rate aFtER wE Have chosen the final
mOdEl (MLP sIze anD actual weightS)
 After asseSSINg the finAl moDEl ON the test set, YOU mUST NoT tune the mODel any fuRther!
why separAte test anD VAlidatioN seTs?
THe eRroR RAtE estimate of thE FInal MoDel on vaLidAtion daTa wIll Be bIasEd (Smaller thAn the
truE eRrOr RaTe) sInce tHE validatIoN seT iS usEd to sELect thE finaL modEl
afTEr aSsessinG ThE FInal MoDeL on tHe tEsT set, YoU MUst NOt tune thE model any FurTHer!
sOuRcE : IntrodUction to PatteRn analysis,RiCaRdo gutierREZ-OsunaTeXas A&M University, Texas A&M UNIversitY

A:

TYpicaLly tO perform sUpeRvised LeaRNing, yoU need tWo typEs Of dATA sets: 

iN OnE daTaSet (your "golD STanDard"), yOu Have The inpuT data together with cORreCt/expected outPut; this DATaSet IS uSUalLy DUly PREpared either by hUmans or by cOlLEcTing some daTA in A sEmi-autoMated waY. But yOU must haVe the expeCted outpuT fOr evERY daTa row here bEcause yoU need this FOr supERvIsed lEarNing.
ThE dAta You arE going TO apPly yoUr modeL tO. In manY caseS, tHis is the Data in which you are intereSted in The
 validation set?

I foundthis confu singwhe n I  u se t he n eural networkt oolb ox in Matlab.
It divid ed th er aw d a ta  setinto th r ee p art s: 
tra in i ng  setval idation  set
testset 
I notice inm an y training  or  learning al gor ithm,th e d a ta is  of ten d ivided into 2  parts, t he traini n g set a n d  t he t est set.
My quest i on s  are:

what is  the d if f er e n cebet ween valid at ion s e t and t e st s e t?Is the valida tion set re a lly  speci fi c t o  neura l net wo r k?Or it is op tion al.
To go  furth e r, is t h ere a d iffere nce  be twee n  v al ida ti o n a n dtes t ing  in cont ex tof ma chin e l e arni ng? 

A: 

Tra ining set: aset  ofe xam plesusedforle arnin g: tofit t he  parameters oftheclassifie r
In  th eMulti l ayer P erc ept ron (ML P) case ,  we  w o u l duse the training s et t ofind the  “opti m al ”w eights w it h t he b a c k-pro p ru l eValidati on set :  a  s et of e xa mplesus edtotunet he p aramet ers of a  clas s ifier
 In theM LP case, we w o ul d us e  the  va lidation se t to find  the “o pti m al” n umber  o f  h i dden units or
deter mi ne a s toppi ng point forthe back-p r o p agationalgo r it h m
Test set: aset o f examples used onl y toassess t he perfor m a nce of a  fu lly -tr ain e d  c lassifier
 In t he M LP  case,wewould u sethe  te stto  estimate  the err or  r at eaft er we have cho se n t he  fi nal
m o del (M LP si ze a nd  a c tua l weigh t s) Afte ras sess ing  t he fi nalm ode l on th e test se t,Y OU M US TNOT tun e the model a ny  further!Wh y s eparat e test and  validation sets?
The e r ror rat e e stima te o f the fin almodelonv alidat ion da ta wi ll  be b iased ( sm all er  than thet r ueerror  r ate)  sincethe validation set isused to selec t t he f i n al  mo d el Aft er ass e s sing the finalmodel on t he te st set, YO U  MU ST  NOT tu ne themodel any fur ther!
sou rce : Int ro duct i o n t o PatternAnalysis ,RicardoG utier r ez -Osun aTe xas A& MUni versi ty, Te x asA&M U nivers it y

A:
Typi ca lly to p erform supervised learn ing, y ou ne edtwo types  of dat a sets: 
Inone datase t ( you r "go lds tanda rd") ,  y ouh ave t he i n put datat og eth e r  w ith correct / e x pec ted o utp u t; Thi s da taset is usuallyd uly prepared e ithe r byhum a ns o rby collectingsom ed a ta in ase mi-automate d way. B ut you m ust ha ve the  expect e d  o u tput f or e ver y data ro w h er e  becaus eyo u  needthis  f or sup ervise d  lea r n ing.
The data yo u are g oingt o a pplyyo ur mode l  to.  In many c ases, thisis the  dat a inwhich y ou  are i nte re sted in th e 
 validation_set?

I found_this confusing when I_use the_neural_network toolbox_in_Matlab.
It divided the_raw data set_into three parts:

training set
validation_set
test set

I notice_in_many training or learning algorithm, the data is often divided into 2 parts, the_training_set and_the_test_set.
My questions are:

what is the_difference between validation set and_test set?_
Is the validation set really specific to neural_network?_Or it is_optional.
To go further, is there a difference between validation_and testing in context of machine_learning?

A:

Training set: a_set_of_examples used for learning:_to fit the parameters of the_classifier
 In the Multilayer Perceptron (MLP)_case, we would use the training set_to find the “optimal” weights with_the back-prop rule
Validation set: a_set of_examples used to tune the_parameters of a_classifier
 In_the MLP case,_we would use the validation set_to find the_“optimal” number of hidden units or
determine_a_stopping point for_the_back-propagation_algorithm
Test set:_a set of_examples_used only_to_assess the performance of a fully-trained_classifier
_In the MLP case, we would use_the test to estimate_the_error rate after we_have chosen the final
model (MLP_size and actual weights)
 After assessing_the final_model on_the test set, YOU MUST NOT tune the model any further!
Why_separate test and validation sets?
The error_rate estimate of the_final model_on_validation data will_be_biased (smaller_than the
true error rate) since the validation_set is_used to select the final model
After_assessing the final model_on_the test set, YOU MUST NOT_tune the model any further!
source :_Introduction to Pattern Analysis,Ricardo Gutierrez-OsunaTexas_A&M_University,_Texas A&M University

A:

Typically to perform_supervised learning, you need two types_of data sets:_

In one dataset (your "gold standard"), you_have_the input data together with correct/expected_output;_This dataset is usually duly prepared_either_by_humans or by collecting some_data in a semi-automated way. But_you must have the expected output for every data_row here because_you need this for supervised_learning.
The_data_you are going to apply your model to. In many_cases, this_is the data_in which you are interested in the