Yesterday, I was informed that my paper was accepted for publication in Cognitve System Research . It is on Deep Learning and Punctuated Equilibrium Theory and will be in a special issue edited by Bryan Jones, Herschel Thomas III, and Peter Erdi. Can we predict puntuations with deep neural networks? Probably, yes! More on this, soon.
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At the symposium for Klaus Schubert I presented some ideas on pragmatism and machine learning. Here are the slides: And here come the R-codes: # Truth is that which works. # If it works, it works. library ( randomForest ) ## randomForest 4.6-12 ## Type rfNews() to see new features/changes/bug fixes. x <- rnorm ( 25 ) a <- 5 b <- 2 e <- rnorm ( 25 , 0 , 0.1 ) y <- a + b * x + e plot ( x , y , main = "Ideal Data Generating Process" ) fit <- lm ( y ~ x ) abline ( fit ) ind <- which ( x < quantile ( x ) [ 2 ] ) x [ ind ] <- x [ ind ] + rnorm ( length ( ind ) , 0 , 10 ) fit <- lm ( y ~ x ) plot ( x , y , ylim = c ( min ( c ( y , predict ( fit ) ) ) , max ( c ( y , predict ( fit ) ) ) ) , main = "Measurment Noise" ) abline ( fit ) points ( x , predict ( fit ) , col = "red" ) fit2 <- randomForest ( y ~ x ) points ( x , predict ( fit2 ) , col = "green" , pch = 19...