Supervised Learning

Supervised Learning

chatbot

Overview

Supervised learning involves training a model on labeled data, where the output is known, to make predictions on unseen data. The choice of algorithm depends on the data characteristics and the task.

Techniques

1. Decision Trees

   • Structure: Hierarchical, representing decisions through tests on attribute values.
   • Applications: Classification and regression.
   • Types: Classification trees (discrete outcomes) and regression trees (continuous outcomes).
   • Challenges: Prone to overfitting, especially with deep trees.

2. Random Forests

   • Concept: Ensemble of multiple decision trees trained on different data subsets.
   • Mechanism: Uses a voting process for predictions.
   • Advantages: Mitigates overfitting, improves robustness.
   • Applications: Document classification, bioinformatics, image classification.

3. Support Vector Machines (SVM)

   • Purpose: Classification by finding an optimal separation line (hyperplane).
   • Features: Maximizes margin between classes, uses kernel functions for non-linear data.
   • Methods: Multi-class problems handled by “One-against-all” and “One-against-one”.
   • Applications: Face recognition, text categorization.

4. Perceptron

   • Type: Simple neural network for binary classification.
   • Mechanism: Computes a weighted sum of inputs, uses an activation function.
   • Limitations: Can only learn linear functions.

5. Multilayer Perceptron (MLP)

   • Structure: Multilayer neural networks for non-linear functions.
   • Training: Uses gradient-based optimization (backpropagation).

Key Concepts

• Overfitting: A model becomes too specialized to training data, reducing generalization.
• Kernel Functions: Used in SVMs to map data into higher-dimensional spaces for linear separation.

Applications

Supervised learning techniques are applied in various fields, including document classification, bioinformatics, image classification, face recognition, and text categorization.

See also

Status:
Tags: science
Superlink: 611 📠Machine Learning
610 🤖Artificial Intelligence, Künstliche Intelligenz

Quellen

Sources:

• Supervised Learning
• Support Vector Machines
• 30 Days of Data Science — Day 1 Regression Problems
• Methods of AI Lecture
• How to Become Very Good at Machine Learning

Erstellt: 14-02-25 15:46