For a problem that is encoded with ni input nodes, no output (feature) nodes, H layers of hidden OCR Output The only restriction of the complexity for an application is set by available memory and CPU time. Restriction of complexity of the problem can be used for any pattern recognition problem area. used with success for heavy quark tagging and quark-gluon separation, it is of general nature and package was originally mainly intended for jet triggering applications [2, 3, 4], where it has been must be loaded with a main application specific program supplied by the user. Even though the 3. 0 package consists of a number of subroutines, most of which handle training and test data, that is pointed out. The self-organizing part is unchanged and is hence not described here. The JETNET this manual and the relation between the underlying algorithms and standard statistical methods networks. A set of rules-of-thumb on when, why and how to use the various options is presented in versions and contains a number of powerful elaborate options for updating and analyzing MLP map algorithm as well. The present version, JETNET 3.0, is backwards compatible with older versions of such networks using the back-propagation updating rule, and included a self-organizing their simplicity and excellent performance. The F77 package J ETNET 2 .0 [1] implemented "vanilla" methods. In particular feed-forward multilayer perceptron (MLP) networks are widely used due to Artificial Neural Networks (ANN) constitute powerful nonlinear extensions of the conventional Method of solution Fischer discriminants, principal components analysis and ARMA models. control. Standard methods for such problems are typically confined to linear dependencies like ing from off-line and on-line parton (or other constituent) identification tasks to accelerator beam Challenging pattern recognition and non-linear modeling problems within high energy physics, rang Nature of physical problem Keywords: pattern recognition, jet identification, data analysis, artificial neural network CPC Library subroutines used: none No. of lines in combined program and test deck: 5753 Peripherals used: terminal for input, terminal or printer for output No. of bits in a word: 32 High speed storage required: M 90k words Program language used: FORTRAN 77 • Langevin Updating [6] OCR Output • Standard Gradient Descent (back-propagation) [5] The following learning algorithms are included in JETNET 3. O: performance and estimating error surfaces. cern additional learning algorithm variants, learning parameters and various tools for gauging [1, 4] for information on this part. For the MLP the most important additions and changes con the self-organizing networks nothing is changed in JETNET 3.0 and we refer the reader to refs. dating and self-organizing networks. Both these approaches were implemented in JETNET 2 .0. For used architectures and procedures are the Multilayer Perceptron (MLP) with backpropagation up is no exception with its demanding on-line and off-line analysis tasks. To date, the ...
Details of the algorithms are described in supporting information available from the Halmstad University website www.hh.se/staff/bioinf/
thorsteinn.rognvaldsson@hh.se.
The datasets used are available at http://www.hh.se/staff/bioinf/
Methods and results are presented for applying supervised machine learning techniques to the task of predicting the need for repairs of air compressors in commercial trucks and buses. Prediction models are derived from logged on-board data that are downloaded during workshop visits and have been collected over three years on large number of vehicles. A number of issues are identified with the data sources, many of which originate from the fact that the data sources were not designed for data mining. Nevertheless, exploiting this available data is very important for the automotive industry as means to quickly introduce predictive maintenance solutions. It is shown on a large data set from heavy duty trucks in normal operation how this can be done and generate a profit.Random forest is used as the classifier algorithm, together with two methods for feature selection whose results are compared to a human expert. The machine learning based features outperform the human expert features, wich supports the idea to use data mining to improve maintenance operations in this domain.
In order to maximize protein identification by peptide mass fingerprinting noise peaks must be removed from spectra and recalibration is often required. The preprocessing of the spectra before database searching is essential but is time-consuming. Nevertheless, the optimal database search parameters often vary over a batch of samples. For high-throughput protein identification, these factors should be set automatically, with no or little human intervention. In the present work automated batch filtering and recalibration using a statistical filter is described. The filter is combined with multiple data searches that are performed automatically. We show that, using several hundred protein digests, protein identification rates could be more than doubled, compared to standard database searching. Furthermore, automated large-scale in-gel digestion of proteins with endoproteinase LysC, and matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) analysis, followed by subsequent trypsin digestion and MALDI-TOF analysis were performed. Several proteins could be identified only after digestion with one of the enzymes, and some less significant protein identifications were confirmed after digestion with the other enzyme. The results indicate that identification of especially small and low-abundance proteins could be significantly improved after sequential digestions with two enzymes.
Using a neural-network classifier we are able to separate gluon from quark jets originating from Monte Carlo-generated e + e ~ events with 85%-90% accuracy.PACS numbers: 13.87.Fh, 12.38.Qk, 13.65.+i In this Letter, we demonstrate how to separate gluon and quark jets using a neural-network identifier. Being able to distinguish the origin of a jet of hadrons is important from many perspectives. It can shed experimental light on the confinement mechanism in terms of detailed studies on the so-called string effect l and related issues. Also, a fairly precise identification of the gluon jet is required for establishing the existence of the three-gluon coupling in e + e~ annihilation. 2 To date the gluon-jet identification has been done by making various cuts on the kinematic variables ranging from just identifying the jet with smallest energy as the gluon jet 1 to more elaborate schemes. 3,4 Such procedures are often based on the entire event rather than just a single isolated jet. It would be desirable to focus on the latter alternative given that in many situations "global" quantities like total jet energies are less well known. One such example is jets produced in high-pr hadron-hadron collisions.A straightforward method for identifying the jets would be to find the functional mapping between the observed hadronic kinematical information and the feature (quark or gluon). This reduces the problem from an expert's exercise to a "black box" fitting procedure. This is exactly what the neural-network approach aims at. It has the advantage over other fitting schemes in that it is very general, inherently parallel, and easy to implement in custom-made hardware with its simple processor structure. The latter feature is very important for realtime triggering.We confine our studies to Monte Carlo-generated e + e~ events using the Lund Monte Carlo model. To some extent this induces a "chicken-and-egg" effect to our studies; some of the physics one wants to study is already there. This effect can be minimized by limiting ourselves to kinematical quantities that are most model independent, e.g., considering the fastest particles only.Although this paper is limited to the separation of gluon and quark jets, it is clear that the methodology could be used in a variety of different triggering situations.The neural-network learning algorithm.-The basic ingredients in a neural network are neurons n t and connectivity weights coij. For feature recognition problems like ours the neurons are often organized in a feedforward layered architecture (see Fig. l) with input
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