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AZHAGARAMMAL, M.SC(INFO TECH)
NADAR SARASWATHI COLLEGE OF
ARTS AND SCIENCE THENI
LEARING METHODS
HEADING
SUPERVISED LEARNING
 In this,every input pattern that is
used to
Train the network is associated with an
output pattern.
 The error can then be change
network parameter.
 Which result in an improvement in
performance.
UNSUPERVISED LEARNING
 In this learning methods, the target
output is not presented to the
network.
 It is as if there is no teacher to
present the patterns and hence.
REINFORCED LEARNING
 In this methods, a teacher though
available does not present the
expected answer but only indicated
if the computed output is correct or
incorrect.
 Supervised and unsupervised
learning methods, which are most
popular from of learning.
HEBBIAN LEARNING
 This rule was proposed by Hebb
(1949) and is based on correlative
we weight adjustment .
 This is the oldest learning
mechanism inspired by biology.
 In this,the input-output pattern
pairs(xi,yi) are associated weight
matrix w, known as the correlation
maatrix.
GRADIENT DESCENT LEARNING:
This is based on the minimization of error E
defined in terms of weights and the activation
function of the network.
Also it is required that the activation function
employed by the network is differentiable as
the weight update is dependent on the
gradient of the error E.
WIJ=N∂E
Weight update is the on link connecting
the jyh necuron of the two neighbouring
layers, then
THANK YOU

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learning method

  • 1. AZHAGARAMMAL, M.SC(INFO TECH) NADAR SARASWATHI COLLEGE OF ARTS AND SCIENCE THENI LEARING METHODS
  • 3. SUPERVISED LEARNING  In this,every input pattern that is used to Train the network is associated with an output pattern.  The error can then be change network parameter.  Which result in an improvement in performance.
  • 4. UNSUPERVISED LEARNING  In this learning methods, the target output is not presented to the network.  It is as if there is no teacher to present the patterns and hence.
  • 5. REINFORCED LEARNING  In this methods, a teacher though available does not present the expected answer but only indicated if the computed output is correct or incorrect.  Supervised and unsupervised learning methods, which are most popular from of learning.
  • 6. HEBBIAN LEARNING  This rule was proposed by Hebb (1949) and is based on correlative we weight adjustment .  This is the oldest learning mechanism inspired by biology.  In this,the input-output pattern pairs(xi,yi) are associated weight matrix w, known as the correlation maatrix.
  • 7. GRADIENT DESCENT LEARNING: This is based on the minimization of error E defined in terms of weights and the activation function of the network. Also it is required that the activation function employed by the network is differentiable as the weight update is dependent on the gradient of the error E.
  • 8. WIJ=N∂E Weight update is the on link connecting the jyh necuron of the two neighbouring layers, then