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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
GRAMMATICAL EVOLUTION FOR FEATURE EXTRACTION IN LOCAL THRESHOLDING PROBLEM
The various lighting intensity in a document image causes diffculty to threshold the image. The conventional statistic approach is not robust to solve such a problem. There should be different threshold value for each part of the image. The threshold value of each image part can be looked as classifcation problem. In such a classifcation problem, it is needed to find the best features. This paper propose a new approach of how to use grammatical evolution to extract those features. In the proposed method, the goodness of each feature is calculated independently. The best features then used for classification task instead of original features. In our experiment, the usage of the new features produce a very good result, since there are only 5 miss-classification of 45 cases.
Variasi intensitas pencahayaan pada citra dokumen akan menyebabkan kesulitan dalam menentukan nilai threshold dari citra tersebut. Pendekatan statistik konvensional tidak cukup baik dalam memecahkan masalah ini. Dalam hal ini, diperlukan nilai threshold yang berbeda-beda untuk setiap bagian citra. Nilai threshold dari setiap bagian citra dapat dipandang sebagai masalah klasifikasi. Dalam permasalahan klasifikasi semacam ini, dibutuhkan pencarian fitur-fitur terbaik. Di sini diusulkan sebuah pendekatan baru untuk mengekstrak fitur-fitur tersebut dengan menggunakan grammatical evolution. Nilai kebaikan dari masing-masing fitur akan dihitung secara saling lepas. Dalam percobaan yang dilakukan, tampak bahwa penggunaan fitur-fitur baru tersebut menghasilkan hasil yang sangat baik. Hanya ditemukan 5 kesalahan pengklasifikasian dalam 45 kasus
Consistent Feature Construction with Constrained Genetic Programming for Experimental Physics
A good feature representation is a determinant factor to achieve high
performance for many machine learning algorithms in terms of classification.
This is especially true for techniques that do not build complex internal
representations of data (e.g. decision trees, in contrast to deep neural
networks). To transform the feature space, feature construction techniques
build new high-level features from the original ones. Among these techniques,
Genetic Programming is a good candidate to provide interpretable features
required for data analysis in high energy physics. Classically, original
features or higher-level features based on physics first principles are used as
inputs for training. However, physicists would benefit from an automatic and
interpretable feature construction for the classification of particle collision
events.
Our main contribution consists in combining different aspects of Genetic
Programming and applying them to feature construction for experimental physics.
In particular, to be applicable to physics, dimensional consistency is enforced
using grammars.
Results of experiments on three physics datasets show that the constructed
features can bring a significant gain to the classification accuracy. To the
best of our knowledge, it is the first time a method is proposed for
interpretable feature construction with units of measurement, and that experts
in high-energy physics validate the overall approach as well as the
interpretability of the built features.Comment: Accepted in this version to CEC 201
From Physical Aggression to Verbal Behavior: Language Evolution and Self-Domestication Feedback Loop
Parameter analysis of copper-nickel-tungsten prepared via powder metallurgy process for electrical discharge machining of polycrystalline diamond
Polycrystalline Diamond (PCD) tools have an outstanding wear resistance. The electric conductivity of PCD caused by the conductive binding material (Cobalt) makes it possible to machine PCD tools with EDM. Electrode used in EDM of PCD must have better porosity, electrical and thermal conductivity. Therefore, this research presents the works in production of Cu-Ni-W electrode by powder metallurgy route. Production of powder metallurgy parts involve mixing of the powder with additives or lubricants, compacting the mixture and heating the green compacts in an Argon gas furnace so the particle bond to each other. Two levels of full factorial with six centre points and two replication technique was used to study the influence of main and interaction effects of the powder metallurgy parameter. There were four factors involved in this experiment. Factor A which is Type of Cu-Ni; Type A and Type B was defined as categorical factor. Factor B in which Composition of W; 5 Wt.%, 15 Wt. % and 25 Wt.%, was defined as numerical factor. Factor C which is the Compaction load; 7, 8 and 9 tonne and Factor D which is Sintering temperature; 635 ℃, 685 ℃ and 735 ℃ were also defined as numerical factor. Optical Microscope, Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray (EDX) was used to analysed the microstructure and surface morphology of Cu-Ni-W electrode. The best parameter combination to produced better porosity, electrical and thermal conductivity for both Type A and Type B was 5 Wt.% of W, compaction load at 9 tonne and sintering temperature at 735℃. The best response for Type A is 12.65% of porosity, 14.40 IACS% of electrical conductivity and 413.26 W/m.℃ of thermal conductivity. While that, the best response for Type B were 9.36% of porosity, 16.66 IACS% of electrical conductivity and 345.21W/m.℃ of thermal conductivity. From the calculation of Maxwell’s Equation, Type A and Type B had the highest electrical conductivity of 58.48 IACS% and 77.35 IACS% respectively at W content of 5Wt.%. Type A and Type B also had the highest thermal conductivity of 369.86 W/m.℃ and 310.24 W/m.℃ respectively at W content of 5 Wt.%. Besides that, thermal conductivity also increased with the temperature increased until 450℃
A Survey on Compiler Autotuning using Machine Learning
Since the mid-1990s, researchers have been trying to use machine-learning
based approaches to solve a number of different compiler optimization problems.
These techniques primarily enhance the quality of the obtained results and,
more importantly, make it feasible to tackle two main compiler optimization
problems: optimization selection (choosing which optimizations to apply) and
phase-ordering (choosing the order of applying optimizations). The compiler
optimization space continues to grow due to the advancement of applications,
increasing number of compiler optimizations, and new target architectures.
Generic optimization passes in compilers cannot fully leverage newly introduced
optimizations and, therefore, cannot keep up with the pace of increasing
options. This survey summarizes and classifies the recent advances in using
machine learning for the compiler optimization field, particularly on the two
major problems of (1) selecting the best optimizations and (2) the
phase-ordering of optimizations. The survey highlights the approaches taken so
far, the obtained results, the fine-grain classification among different
approaches and finally, the influential papers of the field.Comment: version 5.0 (updated on September 2018)- Preprint Version For our
Accepted Journal @ ACM CSUR 2018 (42 pages) - This survey will be updated
quarterly here (Send me your new published papers to be added in the
subsequent version) History: Received November 2016; Revised August 2017;
Revised February 2018; Accepted March 2018
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