Nelder-Mead method is a derivatives-free numerical minimization (maximization) algorithm that is popular among practitioners. In today’s post I will introduce the algorithm, briefly discuss ways it can be modified to suit various optimization problems and implement a variation of the algorithm in VBA. The Algorithm: Since Nelder-Mead (NM) is a derivatives-free algorithm it can be … More Nelder-Mead Method in VBA
The previous two posts have dealt with hard and soft margin SVM. In both cases our model used a linear (hyperplane) decision boundary. The only difference between the two is that the soft margin classifier does not split the two classes perfectly because the data is not linearly separable. We still used a hyperplane but … More Support Vector Machine Without Tears- Part3 [Kernel Trick]
Today I will continue with the topic of SVM and extend the discussion to include classification problems where the data is not linearly separable. In the previous post I described the hard margin classifier where we derived its mathematical formulation and implemented it in a spreadsheet. Hard Margin Classifier Recap We decided to use a … More Support Vector Machines Without Tears – Part 2 [Soft Margin]
I have been on a machine learning MOOCS binge in the last year. I must say some are really amazing. The one weakness so far is the treatment of support vector machines (SVM). It’s a shame really since other popular classification algorithms are covered. I should mention that there are two exceptions, Andrew Ng’s Machine … More Support Vector Machines Without Tears – Part 1 [Hard Margin]
Today’s post is about a phenomena observed in many “naturally” occurring data collections that can be exploited in accounting fraud detection. This phenomena is referred to as Benford’s Law and is named after Frank Benford who popularised it. The original discovery is actually credited to Simon Newcomb. The main idea is that leading digits in … More Fraud Detection with Benford’s Law
Haven’t had the time to add posts recently due to traveling plans but I’m back for a week and have sketched out a plan for a series of posts on predictive modeling. I recently received a fantastic bday present in the form of Applied Predictive Modeling by Max Kuhn and Kjell Johnson and highly recommended … More Ridge Regression in Excel/VBA
In this post I wanted to present a very popular clustering algorithm used in machine learning. The k-means algorithm is an unsupervised algorithm that allocates unlabeled data into a preselected number of K clusters. A stylized example is presented below to help with the exposition. Lets say we have 256 observations which are plotted below. … More K-Means Clustering in Excel
Today will be a quick post that is part of a series on FX volatility skew in G10. To do that we will have a look at implied versus realised skew. To decompose realised and implied skew we will use the following definitions. Implied Skew is the change in implied volatility that is priced into … More FX Skew ~ PART I
I am back after a two week trip to the land of twisted melodramatic tv series, androgynous boy bands, delicious food, and perennial jingoistic posturing by a divided people. I am obviously talking about Korea. Was such a pleasure to visit with family and bounce around Seoul. Now it’s time to catch up with the … More Akima Spline Interpolation in Excel
Today we wanted to add a short follow up on our previous post on artificial neural networks. The example which we gave of training an ANN on XOR function probably appears to be very trivial for the readers. Today we want to show another example that may highlight how neural networks can be used to … More Pattern Recognition with Neural Network in Excel (a toy example)
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