Possible Extensions for State of the Art Machine Learning Methods

-Relax the assumptions
-Put in a hierarchy
-Semi,hybrid version
-Active,online version
-Better performance
-Relation with other methods
-More efficient
-A more general version
-Application on more realistic problem or different domain

Evalution metrics using Javascript

function getEvaluationMetrics(tp,fp,tn,fn){ 
precision=(tp/(tp+fp)); 
recall=(tp/(tp+fn)); 
f1=2*(precision*recall/(precision+recall)); 
var m= new Array(3); m[0]=precision; m[1]=recall; m[2]=f1; 
return m; 
}

Paradox

nothing is exactly true -> Altough we observe many things that is not perfect , this hypothesis could not be true for any hypothesis set.

Logistic Fit

http://www.mathworks.com/help/stats/glmfit.html
http://nlp.stanford.edu/~manning/courses/ling289/logistic.pdf

R/Matlab Anonymous functions

R for variable X:
means <- function(x)  rnorm(1,x)

Matlab
means = @(x) x+randn(1)

Stationary distribution , probability flow analogy

In page rank we are multiplying Transpose Adjacency matrix with current page rank vector. If the result is stationary distribution it means it will converge and all values will stay same. We can think this as incoming probability to a node is equal to outgoing probability.


For detailed balance condition T(x'->x)P(x') = T(x->x')P(x) if the graph is undirected
So total flow of x' times fraction of flow to x T(x'->x) is equal to total flow of x times the fraction of flow from x to x' is equal means incoming and outgoing probability flow in the full duplex edge.

It is possible that, this concept is already originating from real physical phenomena such as fluid flow or air flow.

Bayesian F1

Decision theory considers  the posterior probability to decide which class to chose , or weight using a cost function. However in this setting its objective is the accuracy. Most often we are more interested in more balanced measures such as F1 due to skewed classes. How would be F1 maximizer Bayesian decision mechanism if the distribution is exactly known ? I guess this is already implemented in somewhere , however I didn't face it yet.

Latex 2 column figure

\begin{figure*}[ht]
\vskip 0.2in
\begin{center}
\centerline{\includegraphics[width=2\columnwidth]{NAME}}
\caption{NAME}
\end{center}
\vskip -0.2in
\end{figure*}

More appealing

Novel, hierarchical , jointly learning , hybrid , semi supervised , hyper-parameters , large data , distributed , active , cost sensitive.