Wyniki wyszukiwania - BazTech

1

Indukcja drzewa decyzyjnego do oceny strat ciśnienia na przewodach sieci wodociągowych

Czapczuk A., Dawidowicz J.

Gaz, Woda i Technika Sanitarna

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2015

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Nr 1

11--14

PL

W artykule zaprezentowano proces indukcji drzewa decyzyjnego metodą C4.5 przeznaczonego do oceny strat ciśnienia na przewodach wodociągowych. Zamieszczono schemat uzyskanego drzewa decyzyjnego oraz wyniki funkcjonowania reguł drzewa w postaci macierzy błędów. Reguły decyzyjne wygenerowane za pomocą drzewa mogą być wykorzystywane w systemach komputerowych, będących połączeniem systemu ekspertowego oraz tradycyjnego programu do obliczeń hydraulicznych systemów dystrybucji wody.

EN

Problem discussed in this paper is the evaluation of the pressure losses in calculation sections, which indirectly relates to the selection of pipe diameters, lengths of lines and absolute roughness coefficient. Make sure that the pressure losses are not too high, resulting in a decline of the hydraulic grade line, often below the terrain or too low, due to oversizing pipe diameters. The paper presents the process of induction C4.5 decision tree method designed to assess the pressure losses on water pipes. A scheme of the decision tree obtained and the results of the operation of the rules of the trees in the form of a confusion matrix. Decision rules made in this paper can be used in computer systems, which is a combination of expert system and the traditional program for hydraulic calculations of water distribution systems.

2

On Principles of Software Engineering - Role of the Inductive Inference

Samuelis L.

e-Informatica Software Engineering Journal

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2012

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Vol. 6, nr 1

71--77

EN

This paper highlights the role of the inductive inference principle in software engineering. It takes the challenge to settle difierences and to confront the ideas behind the usual software engineering concepts. We focus on the inductive inference mechanism's role behind the automatic program construction activities and software evolution. We believe that the revision of rather ln old ideas in the new context of software engineering could enhance our endeavour and that is why deserves more attention.

3

Learning Behaviors of Functions

Kalyanasundaram B., Velauthapillai M.

Fundamenta Informaticae

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2010

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Vol. 98, nr 2/3

183-198

EN

We consider the inductive inference model of Gold [15]. Suppose we are given a set of functions that are learnable with certain number of mind changes and errors. What properties of these functions are learnable if we allow fewer number of mind changes or errors? In order to answer this question this paper extends the Inductive Inference model introduced by Gold [15]. Another motivation for this extension is to understand and characterize properties that are learnable for a given set of functions. Our extension considers a wide range of properties of function based on their input-output relationship. Two specific properties of functions are studied in this paper. The first property, which we call modality, explores how the output of a function fluctuates. For example, consider a function that predicts the price of a stock. A brokerage company buys and sells stocks very often in a day for its clients with the intent of maximizing their profit. If the company is able predict the trend of the stock market "reasonably" accurately then it is bound to be very successful. Identification criterion for this property of a function f is called PREX which predicts if f(x) is equal to, less than or greater than f(x + 1) for each x. Next, as opposed to a constant tracking by a brokerage company, an individual investor does not often track dynamic changes in stock values. Instead, the investor would like to move the investment to a less risky option when the investment exceeds or falls below certain threshold. We capture this notion using an identification criterion called TREX that essentially predicts if a function value is at, above, or below a threshold value. Conceptually,modality prediction (i.e., PREX) and threshold prediction (i.e., TREX) are "easier" than EX learning. We show that neither the number of errors nor the number of mind-changes can be reduced when we ease the learning criterion from exact learning to learning modality or threshold. We also prove that PREX and TREX are totally different properties to predict. That is, the strategy for a brokerage company may not be a good strategy for individual investor and vice versa.

4

Prescribed Learning of Indexed Families

Jain S., Stephan F., Nan Y.

Fundamenta Informaticae

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2008

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Vol. 83, nr 1-2

159-175

EN

This work extends studies of Angluin, Lange and Zeugmann on how learnability of a language class depends on the hypothesis space used by the learner. While previous studies mainly focused on the case where the learner chooses a particular hypothesis space, the goal of this work is to investigate the case where the learner has to cope with all possible hypothesis spaces. In that sense, the present work combines the approach of Angluin, Lange and Zeugmann with the question of how a learner can be synthesized. The investigation for the case of uniformly r.e. classes has been done by Jain, Stephan and Ye [6]. This paper investigates the case for indexed families and gives a special attention to the notions of conservative and non U-shaped learning.

5

Capabilities of Thoughtful Machines

Kalyanasundaram B., Velauthapillai M.

Fundamenta Informaticae

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2006

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Vol. 74, nr 2,3

329-340

EN

When learning a concept the learner produces conjectures about the concept he learns. Typically the learner contemplates, performs some experiments, make observations, does some computation, thinks carefully (that is not output a new conjecture for a while) and then makes a conjecture about the (unknown) concept. Any new conjecture of an intelligent learner should be valid for at least some ``reasonable amount of time'' before some evidence is found that the conjecture is false. Then maybe the learner can further study and explore the concept more and produce a new conjecture that again will be valid for some ``reasonable amount of time''. In this paper we formalize the idea of reasonable amount of time. The learners who obey the above constraint are called ``Thoughtful learners '' (TEX learners). We show that there are classes that can be learned using the above model. We also compare this leaning paradigm to other existing ones. The surprising result is that there is no capability intervals in team TEX-type learning. On the other hand, capability intervals exist in all other models. Also these learners are orthogonal to the learners that have been studied in the literature.