We demonstrate a hybrid machine learning method to classify schizophrenia patients and healthy controls, using functional magnetic resonance imaging (fMRI) and single nucleotide polymorphism (SNP) data. The method consists of four stages: (1) SNPs with the most discriminating information between the healthy controls and schizophrenia patients are selected to construct a support vector machine ensemble (SNP-SVME). The method was evaluated by a fully validated leave-one-out method using 40 subjects (20 patients and 20 controls). The classification accuracy was: 0.74 for SNP-SVME, 0.82 for Voxel-SVME, 0.83 for ICA-SVMC, and 0.87 for Combined SNP-fMRI. Experimental results show that better classification accuracy was achieved by combining genetic and fMRI data than using either alone, indicating that genetic and brain function representing different, but partially complementary aspects, of schizophrenia etiopathology.

Support Vector Data Description (SVDD) is a popular outlier detection technique which constructs a flexible description of the input data. SVDD computation time is high for large training datasets which limits its use in big-data process-monitoring applications. We propose a new iterative sampling-based method for SVDD training. The method incrementally learns the training data description at each iteration by computing SVDD on an independent random sample selected with replacement from the training data set. The experimental results indicate that the proposed method is extremely fast and provides a good data description .

For a data scientist is essential to be familiar with the most important and current fields of research in machine learning and data mining. The algorithms in machine learning and data mining advance to a higher level of accuracy and flexibility and a data scientist should be prepared to implement the best algorithms and methods. The investigation of most common topics in machine learning and data mining provides an insight about the most relevant areas of research. To achieve this goal, I used the database of ScienceDirect.com. ScienceDirect has access to about 2,500 academic journals, more than 26,000 e-books and more than 13 million articles.

I have decided to focus on the field of healthcare and classify whether or not a patient has Parkinson's disease based on their vocalization data. For context, Parkinson's is a progressive disease that causes the degeneration of the brain, leading to both motor and cognitive problems. It is thus reasonable to assume a correlation between a patient's ability to speak and their progression into Parkinson's as these capabilities regress. The data set I worked with was obtained through a 2008 study by the journal, IEEE Transactions on Biomedical Engineering, of how various parameters of voice frequency can help classify if a patient is suffering from Parkinson's. By performing a classification on this data, I hope to prove that vocalization tests are indeed a well suited way to diagnose a patient for this disease.

Ladies and gentlemen, place your bets, today we are going to do our best to beat one of the most frightening opponents that you can face in finance: a monkey. As you probably already know, in this blog we are all quite obsessed with predicting trends and returns, you can find other nice attempts in'Markov Switching Regimes say… bear or bullish?' by mplanaslasa or'Predict returns using historical patterns' by fjrodriguez2. Today, we are trying to predict the sign of tomorrow's return for different currency pairs, and I can assure you that a monkey making random bets on the sign and getting it right 50% of the time is going to be a tough benchmark. We are going to use an off the shelf machine learning algorithm, the support vector classifier. Support Vector Machines are an incredibly powerful method to solve regression and classification tasks.

This is the first article from a series of articles I will be writing about the math behind SVM. There is a lot to talk about and a lot of mathematical backgrounds is often necessary. However, I will try to keep a slow pace and to give in-depth explanations, so that everything is crystal clear, even for beginners. Part 1: What is the goal of the Support Vector Machine (SVM)? Part 2: How to compute the margin?

Examples of algorithms to get you started with WEKA: logistic regression, decision tree, neural network and support vector machine.

An implementation of support vector machines (SVM) is available in the JMSL Numerical Library as of release 7.3. The data mining functionality in JMSL continues to expand with the latest release, including areas such as decision trees and bootstrap aggregation. The documentation of the IMSL Libraries is detailed and robust, but the algorithm discussion and examples can only cover a finite set of use cases. This series of blog posts walks through some additional examples with a focus on classification, starting with the textbook examples part of most SVM resources. Notes and key points are highlighted throughout to provide a complementary resource for users new to SVM or new to using the JMSL Library.

Songbirds provide a model system that neuroscientists use to understand how the brain learns and controls speech and similar skills. Much like infants learning to speak from their parents, songbirds learn their song from a tutor and practice it millions of times before reaching maturity. Also like humans, songbirds have evolved special brain regions for learning and producing their vocalizations. These newly-evolved brain regions in songbirds, known as the song system, are found within broader brain areas shared by birds and humans across evolution. So by studying how the song system works, we can learn about our own brains.

Hierarchical temporal memory (HTM) is an emerging machine learning algorithm, with the potential to provide a means to perform predictions on spatiotemporal data. The algorithm, inspired by the neocortex, currently does not have a comprehensive mathematical framework. This work brings together all aspects of the spatial pooler (SP), a critical learning component in HTM, under a single unifying framework. The primary learning mechanism is explored, where a maximum likelihood estimator for determining the degree of permanence update is proposed. The boosting mechanisms are studied and found to be only relevant during the initial few iterations of the network. Observations are made relating HTM to well-known algorithms such as competitive learning and attribute bagging. Methods are provided for using the SP for classification as well as dimensionality reduction. Empirical evidence verifies that given the proper parameterizations, the SP may be used for feature learning.