Hi SAS Viya Community,
I’m presenting a live “Ask the Expert” webinar on September 26 th , 1 PM – 4 PM ET entitled AI and Machine Learning in Action Using SAS® Viya®: A Detailed Use Case. I’ll use a comprehensive example to show you how Viya can use these capabilities to benefit an organization’s operations. Bring your questions. We’ll host a live Q&A session at the end of the webinar.
You will learn about:
The industry-leading AI and machine-learning capabilities of Viya.
How Viya takes an integrated approach to support the complete analytics life cycle and the collaboration of all roles within the life cycle.
How experts in various roles from data engineer to data scientist can use the high-performance capabilities of Viya using their preferred methods.
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Hi SAS Viya Community,
I’m presenting a live “Ask the Expert” webinar on September 26 th , 1 PM – 4 PM ET entitled AI and Machine Learning in Action Using SAS® Viya®: A Detailed Use Case. I’ll use a comprehensive example to show you how Viya can use these capabilities to benefit an organization’s operations. Bring your questions. We’ll host a live Q&A session at the end of the webinar.
You will learn about:
The industry-leading AI and machine-learning capabilities of Viya.
How Viya takes an integrated approach to support the complete analytics life cycle and the collaboration of all roles within the life cycle.
How experts in various roles from data engineer to data scientist can use the high-performance capabilities of Viya using their preferred methods.
Reserve your spot for the webinar.
Want more tips? Be sure to subscribe to the Ask the Expert board to receive follow up Q&A, slides and recordings from this and other SAS Ask the Expert webinars.
Can't join the live event? You can view this and other Ask the Expert sessions on-demand here.
... View more
I just saw Rob has replied the code for proc optmodel.
Here is the code for proc svm.
data x1x2y;
input x1 x2 y;
datalines;
0.49922066 0.834590569 -1
-0.509598752 0.865356638 -1
-0.313012311 -0.54000741 -1
0.713675823 0.094540165 -1
-1.05062909 -0.493905705 -1
1.326427551 1.400617167 1
0.688668461 1.385509561 1
2.705730511 1.526311825 1
2.335457445 -0.497797479 1
-1.340637901 2.205443013 1
run;
proc dmdb batch
data=x1x2y
dmdbcat=_cat
out=_x1x2y;
var x1 x2;
class y;
run;
proc svm
data=_x1x2y
dmdbcat=_cat
kernel=polynom
c=1
k_par=2;
var x1 x2;
target y;
run;
and the results:
The SAS System
The SVM Procedure
---Start Training: Technique= FQP---
Bias -0.163379279 Applied on Predicted Values
The SAS System
The SVM Procedure
Classification Table (Training Data)
Misclassification=0 Accuracy= 100.00
Observed
Predicted
-1
1
Missing
-1
5.0000
0
0
1
0
5.0000
0
Missing
0
0
0
The SAS System
The SVM Procedure
Support Vector Classification
C_CLAS
Kernel Function
Polynomial
Kernel Function Parameter 1
2.000000
Estimation Method
FQP
Number of Observations (Train)
10
Number of Effects
2
Regularization Parameter C
1.000000
Classification Error (Training)
0
Objective Function
-2.925242
Inf. Norm of Gradient
9.436896E-16
Squared Euclidean Norm of w
2.983253
Geometric Margin
1.157937
Number of Support Vectors
6
Number of S.Vector on Margin
3
Bias
-0.163379
Norm of Longest Vector
3.000000
Radius of Sphere Around SV
2.407486
Estimated VC Dim of Classifier
18.290903
KT Threshold
.
Number of Kernel Calls
189
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It seems this data is not related to optimization problem, such as minimize or maximize y. It seems to be a very traditional classification problem, and then you should use the traditional SVM algorithm.
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PROC OPTMODEL doesn't has a SVM algorithm. It has transitive closure, shortest path, minimum cost network flow, minimum spanning tree, minimum cut, TSP, among others, but not SVM. For SVM you need to use Enterprise Miner or Viya VDMML.
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You can solve it by using Time Delay Neural Networks using PROC NEURAL. You have to create a skip layer perceptron architecture, having input nodes connected to hidden nodes and connected direct to the output node.
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ARIMA model is used to for time series and auto regressive neural networks can also be used for time series if you account for direct connections between the input layers nodes and the output layer node. The directed connections helps you to account for the non stationary data. However, ensemble of ARIMA and neural network is no the same as a auto regressive neural network. Particular in EM ensemble models work in a different way by combining the outcomes, meaning the predict probabilities for interval targets and voting or averaging the posterior probability for categorical targets.
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