Implementation of global optimization algorithms, preferably in python, to trai a SVM in which some of the data has no label Many algorithms exist for S3VM – exact (branch and bound) and heuristic (global optimization). We aim at implementing some
Forecasting time series with Support Vector Regression
A comparison between classical (ARIMA) forecasting methods for time series and regression based on Support Vector Machines. A huge set of economic time series is available to train and validate foreasting methods Skills required: basic computer science skills; python might
Iterative LASSO Framework for feature selection
Feature selection can be performed by the Least Absolute Shrinkage and Selection Operator (LASSO), a regression method which imposes a penalty on the absolute value of the regression coefficients. LASSO presents some limitations since it can’t succeed in recovering the right set of features
Training Support Vector Machines using second order information
The goal of the thesis is to solve the Support Vector Regression problem through second order methods to achieve fast convergence and good accuracy. A primal, unconstrained and twice differentiable formulation of the problem is used. The Representer theorem allows
Optimization algorithms for Recurrent neural networks
Recurrent neural networks (RNNs) are know to be extremely powerful yet effectively impossible to train with standard first order methods when the training sequences exhibit long term dependencies. The aim of this thesis is to develop a novel optimization algorithm