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Helping synchronization between clients and servers accross a network. The client is expected to have pushed its changes to the server and then requests the library to pull changes from the server. Clients of the library must provide on the server a way to serialize items and an endpoint, as well as a way to deserialize items and to access the server endpoint on the client. The algorithm requires O(log(n)) roundtrips to the server and consumes a total bandwidth of O(n) where n is the number of items which changed on the server since the last synchronization

Class implementing the predictive apriori algorithm for mining association rules. It searches with an increasing support threshold for the best 'n' rules concerning a support-based corrected confidence value. For more information see: Tobias Scheffer: Finding Association Rules That Trade Support Optimally against Confidence. In: 5th European Conference on Principles of Data Mining and Knowledge Discovery, 424-435, 2001.

This Content Negotiation Filter provides a content negotiation filter for Java servlet containers. The content negotiation algorithm is based on the Apache Negotiation Algorithm but lacks support for the encoding and character set. The variants can be configured through an XML file, negotiation.xml, in the WEB-INF/classes directory. This file can be overriden with the 'negotiation' filter parameter. This XML file is parsed with the class 'com.mattunderscore.filter.contentnegotiation.parser.NegotiationDOMParser'. This class can be overriden with the 'negotiationParser' filter parameter. It is intended that a more efficient SAX parser will be implemented later. Alternative parsers or XML representations are supported so long as the parser implements the 'com.mattunderscore.filter.contentnegotiation.parser.NegotiationXMLParser' interface and returns a list of 'com.mattunderscore.filter.contentnegotiation.parser.Variant' objects.

Binary-class and multi-class alternating decision trees. For more information see: Freund, Y., Mason, L.: The alternating decision tree learning algorithm. In: Proceeding of the Sixteenth International Conference on Machine Learning, Bled, Slovenia, 124-133, 1999. Geoffrey Holmes, Bernhard Pfahringer, Richard Kirkby, Eibe Frank, Mark Hall: Multiclass alternating decision trees. In: ECML, 161-172, 2001.

Implements a least median squared linear regression utilizing the existing weka LinearRegression class to form predictions. Least squared regression functions are generated from random subsamples of the data. The least squared regression with the lowest meadian squared error is chosen as the final model. The basis of the algorithm is Peter J. Rousseeuw, Annick M. Leroy (1987). Robust regression and outlier detection.

Class implementing a HyperPipe classifier. For each category a HyperPipe is constructed that contains all points of that category (essentially records the attribute bounds observed for each category). Test instances are classified according to the category that "most contains the instance". Does not handle numeric class, or missing values in test cases. Extremely simple algorithm, but has the advantage of being extremely fast, and works quite well when you have "smegloads" of attributes.

Implements Winnow and Balanced Winnow algorithms by Littlestone. For more information, see N. Littlestone (1988). Learning quickly when irrelevant attributes are abound: A new linear threshold algorithm. Machine Learning. 2:285-318; N. Littlestone (1989). Mistake bounds and logarithmic linear-threshold learning algorithms. University of California, Santa Cruz. Does classification for problems with nominal attributes (which it converts into binary attributes)

An implementation of the Ordinal Stochastic Dominance Learner. Further information regarding the OSDL-algorithm can be found in: S. Lievens, B. De Baets, K. Cao-Van (2006). A Probabilistic Framework for the Design of Instance-Based Supervised Ranking Algorithms in an Ordinal Setting. Annals of Operations Research; Kim Cao-Van (2003). Supervised ranking: from semantics to algorithms; Stijn Lievens (2004). Studie en implementatie van instantie-gebaseerde algoritmen voor gesuperviseerd rangschikken

Class implementing a GSP algorithm for discovering sequential patterns in a sequential data set. The attribute identifying the distinct data sequences contained in the set can be determined by the respective option. Furthermore, the set of output results can be restricted by specifying one or more attributes that have to be contained in each element/itemset of a sequence. For further information see: Ramakrishnan Srikant, Rakesh Agrawal (1996). Mining Sequential Patterns: Generalizations and Performance Improvements.