Each year, Risk Quant Europe Conference, a conference well-attended by practitioners from banking, asset management, insurers as well as academics from Europe, selects two papers to present in their annual conference.

For 2018, our paper is lucky to be one of the two winning papers selected by the Advisory Board for the conference to be held in London. Please feel free to check out our paper titled CDS Rate Construction Methods by Machine Learning…

Does it sound familiar to you? In order to get an idea of how to choose a parameter for a given classifier, you have to cross reference to a number of papers or books, which often turn out to present competing arguments for or against a certain parameterization choice but with few applications to real-world problems.

For example, you may find a few papers discussing optimal selection of K in…

Cross Validation is often used as a tool for model selection across classifiers. As discussed in detail in the following paper https://ssrn.com/abstract=2967184, Cross Validation is typically performed in the following steps:

• Step 1: Divide the original sample into K sub samples; each subsample typically has equal sample size and is referred to as one fold, altogether,…

In practice, we often have to make parameterization choices for a given classifier in order to achieve optimal classification performances; just to name a few examples:

• Neural Network: e.g., the optimal choice of Activation Functions, # of hidden units
• Support Vector Machine: e.g., the optimal choice of Kernel Functions
• Ensemble: e.g., the number of Learning Cycles for Bagging.
• Discriminant Analysis: e.g., Linear/Quadratic; regularization…

Past literature show that the comparisons of classifier's performance are specific to the types of datasets (e.g., Pharmaceutical industry data) used; i.e., some classifiers may perform better in some context than others. A paper titled CDS Rate Construction Methods by Machine Learning Techniques conducts the performance comparison exclusively in the context of financial market by applying a wide range of classifiers to provide solution to so-called Shortage of…

Introduction:

Machine Learning is a vast area of Computer Science that is concerned with designing algorithms which form good models of the world around us (the data coming from the world around us).

Within Machine Learning many tasks are - or can be reformulated as - classification tasks.

In classification tasks we are trying to produce a model which can give the correlation…

Reading the academic literature Text Analytics seems difficult. However, applying it in practice has shown us that Text Classification is much easier than it looks. Most of the Classifiers consist of only a few lines of code.In this three-part blog series we will examine the three well-known Classifiers; the Naive Bayes, Maximum Entropy and Support Vector Machines. From the…

Natural language processing (NLP) involves machine learning, artificial intelligence, algorithms and linguistics related to interactions between computers and human languages. One important goal…