Explore the methods for preventing selection bias when we conduct statistical analysis.

# Baeldung Author

## Gabriele De Luca

Gabriele specializes in artificial intelligence and innovation and on the impact of technology on society. He has authored several scientific papers in the sectors of machine learning, natural language processing, network theory, and multi-agent simulations. On Baeldung he contributes to the section on computer science, where he publishes articles on the theory behind machine learning and artificial intelligence.

## Here's what I've written (so far):

### Feature Selection and Reduction for Text Classification

Filed under Deep Learning, Machine Learning

Explore the most common techniques for feature selection and reduction for text classification.

### An Algorithm for Inflating and Deflating Polygons

Filed under Algorithms, Math and Logic

Explore how to inflate or deflate a polygon utilizing homothety and offsetting.

### State Machines: Components, Representations, Applications

Filed under Deep Learning, Machine Learning

Explore the components, representations, and applications of finite-state machines.

### Trade-offs Between Accuracy and the Number of Support Vectors in SVMs

Filed under Deep Learning, Machine Learning

Explore the relationship between the number of support vectors and the performances of a support vector classifier.

### The Difference Between a Method and a Function

Filed under Core Concepts, Programming

Learn a conceptual difference between methods and functions.

### Ackermann Function

Filed under Math and Logic

Explore the Ackermann function and the problems associated with its computation.

### Alternatives of Ten – Binary, Octal, Hexadecimal

Filed under Math and Logic, OS

Explore numeral systems and their associated concepts.

### Ugly Duckling Theorem

Filed under Deep Learning, Machine Learning

Learn about the ugly duckling theorem in its relationship with algorithmic bias.

### Normalize Features of a Table

Filed under Artificial Intelligence

Learn how to normalize the features of a table or dataset.