KeLP is the Kernel-based Learning Platform developed in the Semantic Analytics Group of the University of Roma Tor Vergata.

This is the KeLP core module and it contains the abstract classes and interfaces to work with KeLP.

###Core functionalities

Core functionalities of KeLP comprise the interfaces and abstract classes needed to build and extend the library. The main interfaces and abstract classes are:

• Dataset: it models the notion of a dataset as a collection of examples
• Example: it models a single example as a collection of representations
• Representation: it is the base type for a generic representation
• Label: it models the label
• Kernel: it models the notion of kernel
• LearningAlgorithm: it is the base type for a learning algorithm
• PredictionFunction: it is the base type for a function that compute a prediction

###Learning Algorithms

In this package different subclasses of the LearningAlgorithm interface can be found. The majority of the classes here is not an actual implementation, but they are used to build the hierarchy needed to instantiate the different kind of learning algorithms. For example, BinaryLearningAlgorithm is responsible to model the notion of a learning algorithm that operates with two classes. KernelMethod instead is used to model the notion of learning algorithm based on Kernel functions (e.g. Support Vector Machines).

#####Meta Algorithms Two implementations of meta learning algorithm are in kelp-core. These are the OneVsAllLearningAlgorithm and the OneVsOneLearningAlgorithm. Both model a multi-classification schema, respectively with a One-Vs-All and One-Vs-One strategy. They are based on a base binary learning algorithm and use it to derive a multi-classifier.

###Prediction Functions

The PredictionFunction interface model the notion of function used to make a prediction. Different classes are subtype of PredictionFunction depending on the role they have in classification or regression schemas. For example, BinaryClassifier extends a Classifier that is a prediction function used to derive discrete classifications.

###Kernel functions

Kernel is the base type for modeling a kernel function. Subclasses of kernel model different type of kernel functions available.

• DirectKernel: it models a kernel that operate directly on a specific representation (e.g. a linear kernel or a tree kernel extends this class)
• KernelComposition: it models a kernel function that operate on the result produced by another kernel function. For example, the polynomial or gaussian kernel are instances of this class.
• KernelCombination: it models the basic combination of kernel function, in terms of weighted linear combination of multiple kernel functions.