Machine learning : techniques and foundations

The field of machine learning studies computational methods for acquiring new knowledge, new skills, and new ways to organize existing knowledge. In this paper we present some of the basic techniques and principles that underlie AI research on learning, including methods for learning from examples, learning in problem solving, learning by analogy, grammar acquisition, and machine discovery. In each case, we illustrate the techniques with paradigmatic examples

Effective retrieval and new indexing method for case based reasoning: Application in chemical process design

In this paper we try to improve the retrieval step for case based reasoning for preliminary design. This improvement deals with three major parts of our CBR system. First, in the preliminary design step, some uncertainties like imprecise or unknown values remain in the description of the problem, because they need a deeper analysis to be withdrawn. To deal with this issue, the faced problem description is soften with the fuzzy sets theory. Features are described with a central value, a percentage of imprecision and a relation with respect to the central value. These additional data allow us to build a domain of possible values for each attributes. With this representation, the calculation of the similarity function is impacted, thus the characteristic function is used to calculate the local similarity between two features. Second, we focus our attention on the main goal of the retrieve step in CBR to find relevant cases for adaptation. In this second part, we discuss the assumption of similarity to find the more appropriated case. We put in highlight that in some situations this classical similarity must be improved with further knowledge to facilitate case adaptation. To avoid failure during the adaptation step, we implement a method that couples similarity measurement with adaptability one, in order to approximate the cases utility more accurately. The latter gives deeper information for the reusing of cases. In a last part, we present a generic indexing technique for the base, and a new algorithm for the research of relevant cases in the memory. The sphere indexing algorithm is a domain independent index that has performances equivalent to the decision tree ones. But its main strength is that it puts the current problem in the center of the research area avoiding boundaries issues. All these points are discussed and exemplified through the preliminary design of a chemical engineering unit operation

Taxonomic evidence applying intelligent information algorithm and the principle of maximum entropy: the case of asteroids families

The Numeric Taxonomy aims to group operational taxonomic units in clusters (OTUs or taxons or taxa), using the denominated structure analysis by means of numeric methods. These clusters that constitute families are the purpose of this series of projects and they emerge of the structural analysis, of their phenotypical characteristic, exhibiting the relationships in terms of grades of similarity of the OTUs, employing tools such as i) the Euclidean distance and ii) nearest neighbor techniques. Thus taxonomic evidence is gathered so as to quantify the similarity for each pair of OTUs (pair-group method) obtained from the basic data matrix and in this way the significant concept of spectrum of the OTUs is introduced, being based the same one on the state of their characters. A new taxonomic criterion is thereby formulated and a new approach to Computational Taxonomy is presented, that has been already employed with reference to Data Mining, when apply of Machine Learning techniques, in particular to the C4.5 algorithms, created by Quinlan, the degree of efficiency achieved by the TDIDT family´s algorithms when are generating valid models of the data in classification problems with the Gain of Entropy through Maximum Entropy Principle.Fil: Perichinsky, Gregorio. Universidad de Buenos Aires. Facultad de Ingeniería; ArgentinaFil: Jiménez Rey, Elizabeth Miriam. Universidad de Buenos Aires. Facultad de Ingeniería; ArgentinaFil: Grossi, María Delia. Universidad de Buenos Aires. Facultad de Ingeniería; ArgentinaFil: Vallejos, Félix Anibal. Universidad de Buenos Aires. Facultad de Ingeniería; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; ArgentinaFil: Servetto, Arturo Carlos. Universidad de Buenos Aires. Facultad de Ingeniería; ArgentinaFil: Orellana, Rosa Beatriz. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Plastino, Ángel Luis. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Física; Argentin

Probabilistic Constraint Logic Programming

This paper addresses two central problems for probabilistic processing models: parameter estimation from incomplete data and efficient retrieval of most probable analyses. These questions have been answered satisfactorily only for probabilistic regular and context-free models. We address these problems for a more expressive probabilistic constraint logic programming model. We present a log-linear probability model for probabilistic constraint logic programming. On top of this model we define an algorithm to estimate the parameters and to select the properties of log-linear models from incomplete data. This algorithm is an extension of the improved iterative scaling algorithm of Della-Pietra, Della-Pietra, and Lafferty (1995). Our algorithm applies to log-linear models in general and is accompanied with suitable approximation methods when applied to large data spaces. Furthermore, we present an approach for searching for most probable analyses of the probabilistic constraint logic programming model. This method can be applied to the ambiguity resolution problem in natural language processing applications.Comment: 35 pages, uses sfbart.cl

A taxonomy framework for unsupervised outlier detection techniques for multi-type data sets

The term "outlier" can generally be defined as an observation that is significantly different from the other values in a data set. The outliers may be instances of error or indicate events. The task of outlier detection aims at identifying such outliers in order to improve the analysis of data and further discover interesting and useful knowledge about unusual events within numerous applications domains. In this paper, we report on contemporary unsupervised outlier detection techniques for multiple types of data sets and provide a comprehensive taxonomy framework and two decision trees to select the most suitable technique based on data set. Furthermore, we highlight the advantages, disadvantages and performance issues of each class of outlier detection techniques under this taxonomy framework