5 search results - page 1 / 1 » Learning Random Log-Depth Decision Trees under the Uniform D... |
COLT
2003
Springer
14 years 4 months ago
2003
Springer
We consider three natural models of random logarithmic depth decision trees over Boolean variables. We give an efficient algorithm that for each of these models learns all but an ...
COCO
2006
Springer
14 years 2 months ago
2006
Springer
We give an algorithm that learns any monotone Boolean function f : {-1, 1}n {-1, 1} to any constant accuracy, under the uniform distribution, in time polynomial in n and in the de...
FOCS
2009
IEEE
13 years 8 months ago
2009
IEEE
We give a new model of learning motivated by smoothed analysis (Spielman and Teng, 2001). In this model, we analyze two new algorithms, for PAC-learning DNFs and agnostically learn...
COLT
2001
Springer
14 years 3 months ago
2001
Springer
The Kushilevitz-Mansour (KM) algorithm is an algorithm that finds all the “large” Fourier coefficients of a Boolean function. It is the main tool for learning decision trees ...
STOC
1994
ACM
14 years 3 months ago
1994
ACM
We present new results on the well-studied problem of learning DNF expressions. We prove that an algorithm due to Kushilevitz and Mansour [13] can be used to weakly learn DNF form...