Perceptron - Which step function to choose Announcing the arrival of Valued Associate #679: Cesar Manara Planned maintenance scheduled April 23, 2019 at 23:30 UTC (7:30pm US/Eastern) 2019 Moderator Election Q&A - Questionnaire 2019 Community Moderator Election ResultsProperties for building a Multilayer Perceptron Neural Network using Keras?Perceptron weight vector updateHow to implement gradient descent for a tanh() activation function for a single layer perceptron?Question about Logistic RegressionNormalizing the final weights vector in the upper bound on the Perceptron's convergencePurpose of weights in neural networksIn a convolutional neural network (CNN), when convolving the image, is the operation used the dot product or the sum of element-wise multiplication?Trouble with accuracy of multiclass perceptronOptimizing an averaged perceptron algorithm using numpy and scipy instead of dictionariesMachine Learning Perceptron Algorithm

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Perceptron - Which step function to choose



Announcing the arrival of Valued Associate #679: Cesar Manara
Planned maintenance scheduled April 23, 2019 at 23:30 UTC (7:30pm US/Eastern)
2019 Moderator Election Q&A - Questionnaire
2019 Community Moderator Election ResultsProperties for building a Multilayer Perceptron Neural Network using Keras?Perceptron weight vector updateHow to implement gradient descent for a tanh() activation function for a single layer perceptron?Question about Logistic RegressionNormalizing the final weights vector in the upper bound on the Perceptron's convergencePurpose of weights in neural networksIn a convolutional neural network (CNN), when convolving the image, is the operation used the dot product or the sum of element-wise multiplication?Trouble with accuracy of multiclass perceptronOptimizing an averaged perceptron algorithm using numpy and scipy instead of dictionariesMachine Learning Perceptron Algorithm










2












$begingroup$


I'm studying Perceptron algorithm. Some books use this step function




1 if x>=0 else -1




where x is a dot product between the weights w and a sample x.



Other books use:




1 if x>=0 else 0




What are the practical differences between these step functions?










share|improve this question











$endgroup$




bumped to the homepage by Community 25 mins ago


This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.



















    2












    $begingroup$


    I'm studying Perceptron algorithm. Some books use this step function




    1 if x>=0 else -1




    where x is a dot product between the weights w and a sample x.



    Other books use:




    1 if x>=0 else 0




    What are the practical differences between these step functions?










    share|improve this question











    $endgroup$




    bumped to the homepage by Community 25 mins ago


    This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.

















      2












      2








      2


      2



      $begingroup$


      I'm studying Perceptron algorithm. Some books use this step function




      1 if x>=0 else -1




      where x is a dot product between the weights w and a sample x.



      Other books use:




      1 if x>=0 else 0




      What are the practical differences between these step functions?










      share|improve this question











      $endgroup$




      I'm studying Perceptron algorithm. Some books use this step function




      1 if x>=0 else -1




      where x is a dot product between the weights w and a sample x.



      Other books use:




      1 if x>=0 else 0




      What are the practical differences between these step functions?







      machine-learning neural-network deep-learning perceptron






      share|improve this question















      share|improve this question













      share|improve this question




      share|improve this question








      edited Jan 22 '18 at 1:20









      Vaalizaadeh

      7,62562265




      7,62562265










      asked Dec 31 '17 at 8:54









      PoieraPoiera

      11616




      11616





      bumped to the homepage by Community 25 mins ago


      This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.







      bumped to the homepage by Community 25 mins ago


      This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.






















          2 Answers
          2






          active

          oldest

          votes


















          0












          $begingroup$

          They have the same meaning in this context although during training using Rosenblatt update rule, the former may have great changes during each update. Perceptron is used for binary classification which means there are two possible classes to classify. If the result of inner product, here dot product, is greater than or equal to zero, the class of inputs will be the first class and if it's smaller than zero the class of inputs would be the other class. Perceptron just has one neuron. It's a simple linear classifier. The value of threshold is just important. Means that if the product is greater than zero, the input belongs to e.g. positive class and if is negative it belongs to negative class. The step functions and Rosenblatt update rule are not used any more. They have so much oscillation. Today networks learn using gradient descending algorithms which uses the concept of derivative.



          When you progress, you will see that neural nets which use other activation functions like Sigmoid or Tanh are different. the former has 0.5 expected value and the latter has 0 expected value which causes the second learn so much faster. Although now a days ReLU is more popular among other activation functions.






          share|improve this answer











          $endgroup$




















            0












            $begingroup$

            I think that depends on how the next step in the algorithm is defined in the respective textbook(s). There might be slight differences.



            Your if-statements can be interpreted as the following half-sentences,




            "If there is a change in sign, [update the weights, if there isn't, do
            nothing"]




            .



            The second if statement reads:




            "if the value is nonzero, [update the weights, otherwise do nothing.]"




            Maybe your textbooks differ in how the parts between the [ ... ] are written.






            share|improve this answer









            $endgroup$













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              2 Answers
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              2 Answers
              2






              active

              oldest

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              active

              oldest

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              active

              oldest

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              0












              $begingroup$

              They have the same meaning in this context although during training using Rosenblatt update rule, the former may have great changes during each update. Perceptron is used for binary classification which means there are two possible classes to classify. If the result of inner product, here dot product, is greater than or equal to zero, the class of inputs will be the first class and if it's smaller than zero the class of inputs would be the other class. Perceptron just has one neuron. It's a simple linear classifier. The value of threshold is just important. Means that if the product is greater than zero, the input belongs to e.g. positive class and if is negative it belongs to negative class. The step functions and Rosenblatt update rule are not used any more. They have so much oscillation. Today networks learn using gradient descending algorithms which uses the concept of derivative.



              When you progress, you will see that neural nets which use other activation functions like Sigmoid or Tanh are different. the former has 0.5 expected value and the latter has 0 expected value which causes the second learn so much faster. Although now a days ReLU is more popular among other activation functions.






              share|improve this answer











              $endgroup$

















                0












                $begingroup$

                They have the same meaning in this context although during training using Rosenblatt update rule, the former may have great changes during each update. Perceptron is used for binary classification which means there are two possible classes to classify. If the result of inner product, here dot product, is greater than or equal to zero, the class of inputs will be the first class and if it's smaller than zero the class of inputs would be the other class. Perceptron just has one neuron. It's a simple linear classifier. The value of threshold is just important. Means that if the product is greater than zero, the input belongs to e.g. positive class and if is negative it belongs to negative class. The step functions and Rosenblatt update rule are not used any more. They have so much oscillation. Today networks learn using gradient descending algorithms which uses the concept of derivative.



                When you progress, you will see that neural nets which use other activation functions like Sigmoid or Tanh are different. the former has 0.5 expected value and the latter has 0 expected value which causes the second learn so much faster. Although now a days ReLU is more popular among other activation functions.






                share|improve this answer











                $endgroup$















                  0












                  0








                  0





                  $begingroup$

                  They have the same meaning in this context although during training using Rosenblatt update rule, the former may have great changes during each update. Perceptron is used for binary classification which means there are two possible classes to classify. If the result of inner product, here dot product, is greater than or equal to zero, the class of inputs will be the first class and if it's smaller than zero the class of inputs would be the other class. Perceptron just has one neuron. It's a simple linear classifier. The value of threshold is just important. Means that if the product is greater than zero, the input belongs to e.g. positive class and if is negative it belongs to negative class. The step functions and Rosenblatt update rule are not used any more. They have so much oscillation. Today networks learn using gradient descending algorithms which uses the concept of derivative.



                  When you progress, you will see that neural nets which use other activation functions like Sigmoid or Tanh are different. the former has 0.5 expected value and the latter has 0 expected value which causes the second learn so much faster. Although now a days ReLU is more popular among other activation functions.






                  share|improve this answer











                  $endgroup$



                  They have the same meaning in this context although during training using Rosenblatt update rule, the former may have great changes during each update. Perceptron is used for binary classification which means there are two possible classes to classify. If the result of inner product, here dot product, is greater than or equal to zero, the class of inputs will be the first class and if it's smaller than zero the class of inputs would be the other class. Perceptron just has one neuron. It's a simple linear classifier. The value of threshold is just important. Means that if the product is greater than zero, the input belongs to e.g. positive class and if is negative it belongs to negative class. The step functions and Rosenblatt update rule are not used any more. They have so much oscillation. Today networks learn using gradient descending algorithms which uses the concept of derivative.



                  When you progress, you will see that neural nets which use other activation functions like Sigmoid or Tanh are different. the former has 0.5 expected value and the latter has 0 expected value which causes the second learn so much faster. Although now a days ReLU is more popular among other activation functions.







                  share|improve this answer














                  share|improve this answer



                  share|improve this answer








                  edited Dec 31 '17 at 9:46

























                  answered Dec 31 '17 at 9:21









                  VaalizaadehVaalizaadeh

                  7,62562265




                  7,62562265





















                      0












                      $begingroup$

                      I think that depends on how the next step in the algorithm is defined in the respective textbook(s). There might be slight differences.



                      Your if-statements can be interpreted as the following half-sentences,




                      "If there is a change in sign, [update the weights, if there isn't, do
                      nothing"]




                      .



                      The second if statement reads:




                      "if the value is nonzero, [update the weights, otherwise do nothing.]"




                      Maybe your textbooks differ in how the parts between the [ ... ] are written.






                      share|improve this answer









                      $endgroup$

















                        0












                        $begingroup$

                        I think that depends on how the next step in the algorithm is defined in the respective textbook(s). There might be slight differences.



                        Your if-statements can be interpreted as the following half-sentences,




                        "If there is a change in sign, [update the weights, if there isn't, do
                        nothing"]




                        .



                        The second if statement reads:




                        "if the value is nonzero, [update the weights, otherwise do nothing.]"




                        Maybe your textbooks differ in how the parts between the [ ... ] are written.






                        share|improve this answer









                        $endgroup$















                          0












                          0








                          0





                          $begingroup$

                          I think that depends on how the next step in the algorithm is defined in the respective textbook(s). There might be slight differences.



                          Your if-statements can be interpreted as the following half-sentences,




                          "If there is a change in sign, [update the weights, if there isn't, do
                          nothing"]




                          .



                          The second if statement reads:




                          "if the value is nonzero, [update the weights, otherwise do nothing.]"




                          Maybe your textbooks differ in how the parts between the [ ... ] are written.






                          share|improve this answer









                          $endgroup$



                          I think that depends on how the next step in the algorithm is defined in the respective textbook(s). There might be slight differences.



                          Your if-statements can be interpreted as the following half-sentences,




                          "If there is a change in sign, [update the weights, if there isn't, do
                          nothing"]




                          .



                          The second if statement reads:




                          "if the value is nonzero, [update the weights, otherwise do nothing.]"




                          Maybe your textbooks differ in how the parts between the [ ... ] are written.







                          share|improve this answer












                          share|improve this answer



                          share|improve this answer










                          answered Aug 20 '18 at 13:52









                          knbknb

                          430413




                          430413



























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                              ValueError: Expected n_neighbors <= n_samples, but n_samples = 1, n_neighbors = 6 (SMOTE) The 2019 Stack Overflow Developer Survey Results Are InCan SMOTE be applied over sequence of words (sentences)?ValueError when doing validation with random forestsSMOTE and multi class oversamplingLogic behind SMOTE-NC?ValueError: Error when checking target: expected dense_1 to have shape (7,) but got array with shape (1,)SmoteBoost: Should SMOTE be ran individually for each iteration/tree in the boosting?solving multi-class imbalance classification using smote and OSSUsing SMOTE for Synthetic Data generation to improve performance on unbalanced dataproblem of entry format for a simple model in KerasSVM SMOTE fit_resample() function runs forever with no result