Index
A
- A* algorithm
- about / A brief introduction to the A* algorithm
- implementing / Implementing an A* algorithm
- ARFF file
- converting, to CSV file / Converting an ARFF file to a CSV file
- attributes
- filtering / Filtering attributes
- discretizing / Discretizing attributes
- selecting / Attribute selection
C
- classification
- and regression, differences / Differences between classification and regression
- classifier
- developing / Developing a classifier
- clustering model
- evaluating / Evaluating a clustering model
- CSV file
- converting, to ARFF file / Converting a CSV file to an ARFF file
D
- datasets
- reading / Reading and writing datasets
- writing / Reading and writing datasets
- converting / Converting datasets
- Dijkstra's search algorithm
- implementing / Implementing Dijkstra's search
G
- General Public License (GPL) / An introduction to Weka
- Goal State cube / Understanding the notion of heuristics
I
- Initial State cube / Understanding the notion of heuristics
J
- Java
- used, for setting up Prolog / Setting up Prolog with Java
- used, for Prolog query execution / Executing Prolog queries using Java
- Java Development Kit (JDK)
- installing / Installing JDK and JRE
- installation link / Installing JDK and JRE
- Java libraries
- importing / Importing Java libraries and exporting code in projects as a JAR file
- used, for creating JAR file / Importing Java libraries and exporting code in projects as a JAR file
- Java Runtime Environment (JRE)
- installing / Installing JDK and JRE
- JPL library
- download link / Setting up Prolog with Java
K
- k-means clustering
- working with / Working with k-means clustering
- knowledge engineering bottleneck / What is machine learning?
M
- machine learning / What is machine learning?
- machine learning models
- co-training / Self-training and co-training machine learning models
- self-training / Self-training and co-training machine learning models
- min-max algorithm
- root node / Introducing the min-max algorithm
- leaves / Introducing the min-max algorithm
- about / Introducing the min-max algorithm
- example, implementing / Implementing an example min-max algorithm
- model
- evaluation / Model evaluation
- saving / Loading and saving models
- loading / Loading and saving models
N
- NetBeans IDE
- setting up / Setting up the NetBeans IDE
- reference / Setting up the NetBeans IDE
- nodes
- min nodes / Introducing the min-max algorithm
- max nodes / Introducing the min-max algorithm
- notion of heuristics / Understanding the notion of heuristics
P
- predictions
- making / Making predictions
- creating, with semi-supervised machine learning models / Making predictions with semi-supervised machine learning models
- Prolog
- installation link / Installing Prolog
- installing / Installing Prolog
- used, for rule-based systems / An introduction to rule-based systems with Prolog
- setting, up with Java / Setting up Prolog with Java
- queries, executing with Java / Executing Prolog queries using Java
R
- regression / Differences between classification and regression
- rule-based systems
- using, with Prolog / An introduction to rule-based systems with Prolog
S
- searching / An introduction to searching
- semi-supervised learning
- about / An introduction to semi-supervised learning
- and unsupervised learning, differentiating between / The difference between unsupervised and semi-supervised learning
- semi-supervised machine learning models
- used, for creating predictions / Making predictions with semi-supervised machine learning models
- semi-supervised models
- classifier, creating / Creating a classifier for semi-supervised models
- semi-supervised package
- downloading / Downloading a semi-supervised package
U
- unsupervised learning
- versus semi-supervised learning / The difference between unsupervised and semi-supervised learning
W
- Weka
- about / An introduction to Weka
- reference / An introduction to Weka
- download link / Installing and interfacing with Weka
- installing / Installing and interfacing with Weka
- interfacing with / Installing and interfacing with Weka
- environment, calling into Java / Calling the Weka environment into Java