Add to ORKGWe will start, in our Data Science course, to discuss classification techniques (in the context of supervised models). Consider the following case, with 10 points, and two classes (red and blue) clr1 - c(rgb(1,0,0,1),rgb(0,0,1,1)) clr2 - c(rgb(1,0,0,.2),rgb(0,0,1,.2)) x - c(.4,.55,.65,.9,.1,.35,.5,.15,.2,.85) y - c(.85,.95,.8,.87,.5,.55,.5,.2,.1,.3) z - c(1,1,1,1,1,0,0,1,0,0) df - data.frame(x,y,z) plot(x,y,pch=19,cex=2,col=clr1[z+1]) To get a prediction, i.e. a partition of the spac..
(a) and (b) show the box plot of the five-class classification accuracy with RF and SVM, respectivel...
Data. Consider a set A = {A1,..., An} of attributes, and an additional cate-gorical attribute C, whi...
The past few decades have brought substantial progress in the mathematical analysis of supervised le...
In our data-science class, after discussing limitations of the logistic regression, e.g. the fact th...
Another popular technique for classification (or at least, which used to be popular) is the (linear)...
We consider prediction and classification into diagnostic classes which consist of individuals who c...
In Classification learning, an algorithm is presented with a set of classified examples or ‘‘instanc...
This study focuses on supervised learning, an aspect of statistical learning. The supervised learnin...
This paper introduces a new method to automatically, rapidly and reliably evaluate the class conditi...
We discuss a strategy for polychotomous classification that involves estimating class probabilities ...
This thesis concerns the development and mathematical analysis of statistical procedures for classi...
Abstract In the data preparation phase of data mining, supervised discretization and value grouping ...
Abstract. We introduce a new method for building classification models when we have prior knowledge ...
Machine learning algorithms are used to train the machine to learn on its own and improve from exper...
In this paper we give a survey of the combination of classifiers. We briefly describe basic principl...
(a) and (b) show the box plot of the five-class classification accuracy with RF and SVM, respectivel...
Data. Consider a set A = {A1,..., An} of attributes, and an additional cate-gorical attribute C, whi...
The past few decades have brought substantial progress in the mathematical analysis of supervised le...
In our data-science class, after discussing limitations of the logistic regression, e.g. the fact th...
Another popular technique for classification (or at least, which used to be popular) is the (linear)...
We consider prediction and classification into diagnostic classes which consist of individuals who c...
In Classification learning, an algorithm is presented with a set of classified examples or ‘‘instanc...
This study focuses on supervised learning, an aspect of statistical learning. The supervised learnin...
This paper introduces a new method to automatically, rapidly and reliably evaluate the class conditi...
We discuss a strategy for polychotomous classification that involves estimating class probabilities ...
This thesis concerns the development and mathematical analysis of statistical procedures for classi...
Abstract In the data preparation phase of data mining, supervised discretization and value grouping ...
Abstract. We introduce a new method for building classification models when we have prior knowledge ...
Machine learning algorithms are used to train the machine to learn on its own and improve from exper...
In this paper we give a survey of the combination of classifiers. We briefly describe basic principl...
(a) and (b) show the box plot of the five-class classification accuracy with RF and SVM, respectivel...
Data. Consider a set A = {A1,..., An} of attributes, and an additional cate-gorical attribute C, whi...
The past few decades have brought substantial progress in the mathematical analysis of supervised le...