Mass appraisal is the systematic appraisal of groups of properties as of a given date using standardized procedures and statistical testing. Mass appraisal is commonly used to compute real estate ...tax. There are three traditional real estate valuation methods: the sales comparison approach, income approach, and the cost approach. Mass appraisal models are commonly based on the sales comparison approach. The ordinary least squares (OLS) linear regression is the classical method used to build models in this approach. The method is compared with computational intelligence approaches – support vector machine (SVM) regression, multilayer perceptron (MLP), and a committee of predictors in this paper. All the three predictors are used to build a weighted data-depended committee. A self-organizing map (SOM) generating clusters of value zones is used to obtain the data-dependent aggregation weights. The experimental investigations performed using data cordially provided by the Register center of Lithuania have shown very promising results. The performance of the computational intelligence-based techniques was considerably higher than that obtained using the official real estate models of the Register center. The performance of the committee using the weights based on zones obtained from the SOM was also higher than of that exploiting the real estate value zones provided by the Register center.
Niekam nekyla abejonių dėl to, kad gyvename supami daiktiškos, materialios aplinkos. Jos egzistavimo nereikia įrodinėti, pakanka apžvelgti artimą savo aplinką, paliesti netoli esančius daiktus, ...žmones. Lygiai taip pat neabejojame dėl savo vidinės, sakome – psichinės, dvasinės aplinkos. Tačiau, skirtingai nei su regimais dalykais, neregimieji reikalauja nuolat juos pažinti, nuolat su jais kurti savo individualų arba grupei būdingą santykį. Taip yra su žmogaus vidiniu pasauliu, taip yra ir su socialine aplinka: ji nuolat kinta visuomenei keičiantis, kitados galiojusios tvarkos griūna arba yra peržiūrimos ir įvertinamos naujai.
We do not only collect information by the five senses. The human DNA carries vast amounts of information. Scientists are about to calculate human genes. In the light of the present-day endeavour to ...create artificial intelligence, it is worthwhile to ask how information about a human being is recorded in genes, can such information be decoded by applying, for instance, combined methods of linguistics and other sciences, what percentage of our subconscious archives is made of the information stored in the DNA and whether it yields itself to verbal coding and decoding. The search for the answers to these questions would lead us towards cross-disciplinarity, where geneticists, linguistics and technological scientists could expand the limits of human self-knowledge.
We do not only collect information by the five senses. The human DNA carries vast amounts of information. Scientists are about to calculate human genes. In the light of the present-day endeavour to ...create artificial intelligence, it is worthwhile to ask how information about a human being is recorded in genes, can such information be decoded by applying, for instance, combined methods of linguistics and other sciences, what percentage of our subconscious archives is made of the information stored in the DNA and whether it yields itself to verbal coding and decoding. The search for the answers to these questions would lead us towards cross-disciplinarity, where geneticists, linguistics and technological scientists could expand the limits of human self-knowledge.
Amounts of historical data collected increase and business intelligence applicability with automatic forecasting of time series are in high demand. While no single time series modeling method is ...universal to all types of dynamics, forecasting using an ensemble of several methods is often seen as a compromise. Instead of fixing ensemble diversity and size, we propose to predict these aspects adaptively using meta-learning. Meta-learning here considers two separate random forest regression models, built on 390 time-series features, to rank 22 univariate forecasting methods and recommend ensemble size. The forecasting ensemble is consequently formed from methods ranked as the best, and forecasts are pooled using either simple or weighted average (with a weight corresponding to reciprocal rank). The proposed approach was tested on 12561 micro-economic time-series (expanded to 38633 for various forecasting horizons) of M4 competition where meta-learning outperformed Theta and Comb benchmarks by relative forecasting errors for all data types and horizons. Best overall results were achieved by weighted pooling with a symmetric mean absolute percentage error of 9.21% versus 11.05% obtained using the Theta method.
Šiame straipsnyje yra analizuojamas podėlio sistemos „Redis Cluster“ korektiškumas. Analizuojant sistemą buvo naudojami formalūs metodai – TLA+ specifikavimo kalba buvo sudaryta sistemos formali ...specifikacija. Specifikacijos modelio tikrinimo metu buvo vertinama, ar yra užtikrinama sistemos savybė, kad už vieną maišos lizdą yra atsakingas tik vienas pagrindinis mazgas ir jo pavaldūs mazgai. Atlikus modelio tikrinimą buvo surastos situacijos, kada ši sistemos savybė nėra užtikrinama. Surastos klaidos buvo atkartotos realioje sistemoje ir šioms klaidoms buvo pateikti galimi sprendimo būdai.
Aiškinti pokyčius yra įdomus ir rizikingas užsiėmimas. Dalis pokyčių gali būti apskaičiuojami. Taip veikia, pavyzdžiui, matematikos dėsniai. Dalis pasikeitimų gali būti nuspėjami – todėl mus vilioja ...loterijos ir įvairūs stebuklai. Tačiau didžiausiais stebuklas – mūsų pačių gyvenimas. Jis sunkiai telpa į apskaičiavimus ir spėjimus. Dar didesnė nuostaba apima, kai pradedi mąstyti apie pokyčius, apimančius ne vieno žmogaus, o kartos, amžiaus, tūkstantmečio įvykius.
Abstract
Basic requirements to the methods of current outburst danger prediction are formulated. The methods must be applied in the process of mining not interfering with the works and taking into ...account basic factors that cause the outbursts and they should have scientifically grounded methodology for fast definition of outburst danger criteria under certain mining, geological and engineering conditions. It is shown that geophysical prediction methods can meet the first requirement. Rock pressure, in-situ gas pressure and coal strength are taken as basic factors of outburst danger. Some well-known geophysical method such as a method of acoustic emission, a spectral-acoustic method, a gas analytical method and a temperature method taken separately do not satisfy the second and the third requirements. That is why it is suggested controlling rock pressure by spectral-acoustic method, gas pressure by means of gas analyser on methane concentration at the face of the working while mining coal and coal strength should be measured by a strength measuring device. To improve the accuracy of the prediction a version of spectral-acoustic method based on dependency of amplitude-frequency characteristic median of mining operating equipment noise on average stresses at the section between the source of the noise and geophone. The criterion of outburst danger is substantiated and the algorithm for arranging automated monitoring of coal seam outburst danger is offered.
The option of spectral-acoustic method for coal seam outburst danger prediction based on the influence of outburst danger basic factors on the amplitude-frequency characteristic of acoustic vibration ...median formed when breaking the rock massif by the operating mining equipment is justified. Basic outburst danger factors are: rock pressure, in-situ gas pressure, coal strength.
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