Rule Based Models
A rule based classifier is a model that uses a rule set of “if-then” rules to classify instances. Each rule is expressed in the form: \(r_i : (\text{Cond}_i) \rightarrow y_i\) ....
Post date: 31/01/2026
Association Rule
Association analysis is a field of techniques aimed at extracting interesting relationships hidden in large datasets. A common application of association analysis is for market basket transactions,...
Post date: 15/01/2026
Support Vector Machine
Support vector machines (SVMs) are a set of related supervised learning methods used for classification and regression. They belong to a family of generalized linear classifiers....
Post date: 08/01/2026
Regression
Regression is a common task in supervised learning. While classification assigns to each instance a class (out of a finite set of classes), regression predicts a real-valued number....
Post date: 05/10/2025
K-Nearest Neighbors
A K-NN learner is a type of instance based/lazy classifier. This learner represents each instance in the training set as a point in a n-dimensional space....
Post date: 24/09/2025
Hierarchical clustering
Hierarchical clustering techniques are an important category of clustering methods. They can be divided in agglomeration and division....
Post date: 13/09/2025
Bayes's Theorem
Bayes' Theorem provides a powerful framework for updating our beliefs based on new evidence, making it invaluable across various fields....
Post date: 13/09/2025
Resource Allocation
The Newsvendor Problem. The problems we are going to examine have a forerunner in the Newsvendor Problem, studied as early as 1888. A newspaper seller needs to determine ....
Post date: 11/07/2025
DBSCAN
Density-based spatial clustering of applications with noise (DBSCAN) is a clustering algorithm used in machine learning to partition data into clusters based on their distance to other points. Its effective at identifying and removing noise in a data set, making it useful for data cleaning and outlier detection....
Post date: 05/07/2025
Cluster Analysis: Concepts and Types
Cluster analysis groups data objects into clusters based on the information found in the data itself. The goal is to produce clusters such that all of the members of a single cluster are similar to each other, while objects belonging to different clusters are unrelated...
Post date: 14/05/2025
K-means Clustering
K-means clustering is an unsupervised learning algorithm used for data clustering, which groups unlabeled data points into groups or clusters. It is one of the most popular clustering methods used in machine learning....
Post date: 14/05/2025
Quantitative Modeling (Operations Research - OR) vs Empirical Research
Khi tìm hiểu về chương trình Tiến sĩ (PhD) trong lĩnh vực Quản trị Chuỗi Cung ứng (SCM), mọi người thường nhầm lẫn về Quantitative Modeling (Operations Research - OR) và Empirical Research...
Post date: 13/05/2025