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Everybody. I am entirely new to the topic of classification algorithms, and need a few good pointers about where to start some "serious reading". I am right now in the process of finding out, whether machine learning and automated classification algorithms could be a worthwhile thing to add to some application of mine.

I already scanned through "How to Solve It: Modern heuristics" by Z. Michalewicz and D. Fogel (in particular, the chapters about linear classifiers using neuronal networks), and on the practical side, I am currently looking through the WEKA toolkit source code. My next (planned) step would be to dive into the realm of Bayesian classification algorithms.

Unfortunately, I am lacking a serious theoretical foundation in this area (let alone, having used it in any way as of yet), so any hints at where to look next would be appreciated; in particular, a good introduction of available classification algorithms would be helpful. Being more a craftsman and less a theoretician, the more practical, the better...

Hints, anyone?

by (108k points)

We use a training dataset to get the boundary conditions which could be used to determine each target class. Once the conditions(boundary condition) are determined, the next task is to predict the target class. The whole process is known as classification.

Target class examples:

• Perform the analysis of the customer data to predict whether he will buy computer accessories (Target class: Yes or No)

• Classifying the fruits from features like color, taste, size, weight (Target classes: Apple, Orange, Cherry, Banana)

• Perform the gender classification from hair length (Target classes: Male or Female)

Let us understand the concept of classification algorithms with gender classification using hair length (by no means am I trying to stereotype by gender, this is only an example). To classify gender (target class) using hair length as a feature parameter we could train a model using any classification algorithms to come up with some set of boundary conditions that can be used to differentiate the male and female genders using hair length as the training feature. In the gender classification case, the boundary condition could the proper hair length value. Suppose the differentiated boundary hair length value is 15.0 cm then we can say that if hair length is less than 15.0 cm then gender could be male or else female.

You can refer to the following link for better understanding:

https://medium.com/@Mandysidana/machine-learning-types-of-classification-9497bd4f2e14

by (6.8k points)

Intellipaat Tutorials  to be very useful. They're grounded in solid statistical theory and can be very useful in understanding papers if you select to read them in the future. Here's a short description:

The classification algorithms like decision trees, neural nets, Bayesian classifiers, Support Vector Machine, and cased-based (aka non-parametric) learning. They include regression algorithms like multivariate polynomial regression, MARS, Locally Weighted Regression, GMDH, and neural nets. And they include different data processing operations like clustering (mixture models, k-means and hierarchical), Bayesian networks and Reinforcement Learning.