Text Classification
Text classification is a machine learning technique that categorizes any form of text data. It is used to organize text data better by automating the process of analyzing line by line while categorizing the...
Category: Big Data Analysis
Association Rules
Association rule mining is a methodology that is used to discover unknown relationships hidden in big data. Rules refer to a set of identified frequent itemsets that represent the uncovered relationships in the dataset....
PCA
The Principal Component Analysis (PCA) is a feature engineering technique used with the given dataset before a machine learning algorithm is used or to visually explore the dataset. It emphasizes variation in the dataset...
Cross Validation
Cross validation is a smart technique to perform model selection during the validation process. The model selection performs a decision about a specific machine learning model (e.g. artificial neural network, decision trees, suppor vector...
Sampling Methods
Sampling methods refer to techniques that pick a specifically choosen number of L samples out of a number of N data items in a dataset for data Analysis. More formally ‘sampling methods’ select a...
Kernel
A choosen Kernel is one out of several so-called Kernel methods in machine learning that enable a smart use of non-linear decision boundaries. Such decision boundaries are often necessary since in many cases the...
Gradient Descent
Gradient descent refers to a technique in machine learning that finds a local minimum of a function. It is a quite general optimization technique used in many application areas. It can be used to...
Neural Network
A neural network, more accurately referred to as Artificial Neural Network (ANN), is a quite complex data analysis technique. It is based on a well-defined architecture of many interconnected artificial neurons. But it also...
Sequential Minimal Optimization
The iterative algorithm Sequential Minimal Optimization (SMO) is used for solving quadratic programming (QP) problems. One example where QP problems are relevant is during the training process of support vector machines (SVM). The SMO...
Cross Disciplinary Data Analysis
Cross disciplinary data analysis means that you analyse jointly data sets that origin is within different application domains. It is also known as a data fusion use case of data analysis. It is not...
