For a given time series observation sequence, we can estimate the parameters of the AutoRegression Moving Average (ARMA) model, thereby representing a potentially long time series by a limited dimensional vector. In many applications, these parameter vectors will be separable into different groups, due to the different underlying mechanisms that generate differing time series. We can then use c...

We consider the problem of predicting how a user will continue a given initial text fragment. Intuitively, our goal is to develop a “tab-complete” function for natural language, based on a model that is learned from text data. We consider two learning mechanisms that generate predictive models from collections of application-specific document collections: we develop an N-gram based completion m...

Text mining concerns the discovery of knowledge from unstructured textual data. One important task is the discovery of rules that relate specific words and phrases. Although existing methods for this task learn traditional logical rules, soft-matching methods that utilize word-frequency information generally work better for textual data. This paper presents a rule induction system, TEXTRISE, th...

A persistent trie1-like tree structure is presented as the basis to implement a new classification algorithm of the instance-based learning type that we call Trie-CLASS. Records from a training file are converted into pattern vectors associated with a known class label, forming a hypothesis data model stored permanently into the trie. As a result, disjoint subsets as well as areas of conditiona...

The article describes a method combining two widely-used empirical approaches to learning from examples: rule induction and instance-based learning. In our algorithm (RIONA) decision is predicted not on the basis of the whole support set of all rules matching a test case, but the support set restricted to a neighbourhood of a test case. The size of the optimal neighbourhood is automatically ind...

The paper investigates the memory-based learning (MBL) paradigm as a model of productive linguistic behavior in the domain of Dutch noun plural inflection. We first sketch the origin and background of the MBL approach, to then provide a short overview of Dutch noun plural inflection along with a detailed description of the use of MBL models for inflectional morphology. Results of a large number...

ÐWe develop an automatic text categorization approach and investigate its application to text retrieval. The categorization approach is derived from a combination of a learning paradigm known as instance-based learning and an advanced document retrieval technique known as retrieval feedback. We demonstrate the effectiveness of our categorization approach using two realworld document collections...

User-to-user correlation is a fundamental component of Collaborative Filtering (CF) recommender systems. In user-to-user correlation the importance assigned to each single item rating can be adapted by using item dependent weights. In CF, the item ratings used to make a prediction play the role of features in classical instance-based learning. This paper focuses on item weighting and item selec...

The functional capabilities of the brain are formally characterizable in terms of a finite system along with a memory space which it can manipulate. Two types of learning are possible: ( 1) modification-based learning, associated with alternate realizations of the finite system; (2) memory-based learning, associated with the assimilation, manipulation, and retrieval of memories. Constructive mo...

This paper presents a learning theory pertinent to dynamic decision making (DDM) called instancebased learning theory (IBLT). IBLT proposes five learning mechanisms in the context of a decision-making process: instance-based knowledge, recognition-based retrieval, adaptive strategies, necessity-based choice, and feedback updates. IBLT suggests in DDM people learn with the accumulation and refin...