Concurrent incremental attribute evaluation

The design of a concurrent incremental combined static/dynamic attribute evaluator is presented. The static part is an incremental version of the ordered attribute evaluation scheme. The dynamic part is an incremental version of the dynamic evaluation scheme.To remove the restriction that every transformation of an attributed syntax tree should immediately be followed by a reevaluation of the tree, criteria have been formulated which permit a delay in calling the reevaluator. These criteria allow multiple asynchronous tree transformations and multiple asynchronous reevaluations. Transformation and reevaluation processes are distributed over regions of the tree. Each region is either in its transformation phase or in its reevaluation phase. Different regions can be in different phases at the same time

Rule-based Machine Learning Methods for Functional Prediction

We describe a machine learning method for predicting the value of a real-valued function, given the values of multiple input variables. The method induces solutions from samples in the form of ordered disjunctive normal form (DNF) decision rules. A central objective of the method and representation is the induction of compact, easily interpretable solutions. This rule-based decision model can be extended to search efficiently for similar cases prior to approximating function values. Experimental results on real-world data demonstrate that the new techniques are competitive with existing machine learning and statistical methods and can sometimes yield superior regression performance.Comment: See http://www.jair.org/ for any accompanying file

A unifying approach for queries and updates in deductive databases

This dissertation presents a unifying approach to process (recursive) queries and updates in a deductive database. To improve query performance, a combined top-down and bottom-up evaluation method is used to compile rules into iterative programs that contain relational algebra operators. This method is based on the lemma resolution that retains previous results to guarantee termination.Due to locality in database processing, it is desirable to materialize frequently used queries against views of the database. Unfortunately, if updates are allowed, maintaining materialized view tables becomes a major problem. We propose to materialize views incrementally, as queries are being answered. Hence views in our approach are only partially materialized. For such views, we design algorithms to perform updates only when the underlying view tables are actually affected.We compare our approach to two conventional methods for dealing with views: total materialization and query-modification. The first method materializes the entire view when it is defined while the second recomputes the view on the fly without maintaining any physical view tables. We demonstrate that our approach is a compromise between these two methods and performs better than either one in many situations.It is also desirable to be able to update views just like updating base tables. However, view updates are inherently ambiguous and the semantics of update propagation on recursively defined views were not well understood in the past. Using dynamic logic programming and lemma resolution, we are able to define the semantics of recursive view updates. These are expressed in the form of update translators specified by the database administrator when the view is defined. To guarantee completeness, we identify a subset of safe update translators. We prove that this subset of translators always terminate and are complete

An Incremental Construction of Deep Neuro Fuzzy System for Continual Learning of Non-stationary Data Streams

Existing FNNs are mostly developed under a shallow network configuration having lower generalization power than those of deep structures. This paper proposes a novel self-organizing deep FNN, namely DEVFNN. Fuzzy rules can be automatically extracted from data streams or removed if they play limited role during their lifespan. The structure of the network can be deepened on demand by stacking additional layers using a drift detection method which not only detects the covariate drift, variations of input space, but also accurately identifies the real drift, dynamic changes of both feature space and target space. DEVFNN is developed under the stacked generalization principle via the feature augmentation concept where a recently developed algorithm, namely gClass, drives the hidden layer. It is equipped by an automatic feature selection method which controls activation and deactivation of input attributes to induce varying subsets of input features. A deep network simplification procedure is put forward using the concept of hidden layer merging to prevent uncontrollable growth of dimensionality of input space due to the nature of feature augmentation approach in building a deep network structure. DEVFNN works in the sample-wise fashion and is compatible for data stream applications. The efficacy of DEVFNN has been thoroughly evaluated using seven datasets with non-stationary properties under the prequential test-then-train protocol. It has been compared with four popular continual learning algorithms and its shallow counterpart where DEVFNN demonstrates improvement of classification accuracy. Moreover, it is also shown that the concept drift detection method is an effective tool to control the depth of network structure while the hidden layer merging scenario is capable of simplifying the network complexity of a deep network with negligible compromise of generalization performance.Comment: This paper has been published in IEEE Transactions on Fuzzy System

The structure and formation of natural categories

Categorization and concept formation are critical activities of intelligence. These processes and the conceptual structures that support them raise important issues at the interface of cognitive psychology and artificial intelligence. The work presumes that advances in these and other areas are best facilitated by research methodologies that reward interdisciplinary interaction. In particular, a computational model is described of concept formation and categorization that exploits a rational analysis of basic level effects by Gluck and Corter. Their work provides a clean prescription of human category preferences that is adapted to the task of concept learning. Also, their analysis was extended to account for typicality and fan effects, and speculate on how the concept formation strategies might be extended to other facets of intelligence, such as problem solving

Workshop on the Integration of Finite Element Modeling with Geometric Modeling

The workshop on the Integration of Finite Element Modeling with Geometric Modeling was held on 12 May 1987. It was held to discuss the geometric modeling requirements of the finite element modeling process and to better understand the technical aspects of the integration of these two areas. The 11 papers are presented except for one for which only the abstract is given