Performance Of Different Learning Algorithms Research Articles

Comparison of the performance of different learning algorithms in leaf feature extraction and recognition using convolution neural network

SummaryPlant identification with computer systems has been developed with image processing tools and has helped researchers to identify unknown plant species with high accuracy. In this study, the leaves of five different plants were classified according to their shapes using deep learning. A database was created with leaf images of mint, echinacea, St. John's wort, melissa, and thyme plants. Images in this database were classified with a convolution neural network (CNN). For this classification, 70% training and 30% testing were randomly selected in the database. The parameters of the CNN layer consist of a set of learnable filters. In the CNN, 10 kernel matrices with stride [1 1] were used. A rectified linear unit was chosen as the activation function. Maximum pooling was performed using a filter with stride [2 2]. In this classification, five fully connected layers were created. Using CNN, the performance of different learning algorithms was compared. It was observed that CNN achieved more successful results than traditional attribute methods.

Predicting radiation treatment planning evaluation parameter using artificial intelligence and machine learning

Purpose: This study suggested a new method predicting the dose-volume parameter for radiation treatment planning evaluation using machine learning, and to evaluate the performance of different learning algorithms in the parameter prediction. Methods: Dose distribution index (DDI) for fifty prostate volumetric modulated arc therapy plans were calculated, and compared to results predicted by machine learning using algorithms, namely, linear regression, tree regression, support vector machine (SVM) and Gaussian process regression (GPR). Root mean square error (RMSE), prediction speed and training time were determined to evaluate the performance of each algorithm. Results: From the results, it is found that the square exponential GPR algorithm had the smallest RMSE, relatively high prediction speed and short training time of 0.0038, 4,100 observation/s and 0.18 s, respectively. All linear regression, SVM and GPR algorithms performed well according to their RMSE in the range of 0.0038–0.0193. However, RMSE of the medium and coarse tree regression algorithms were found larger than 0.03, showing that they are not suitable for predicting DDI in this study. Conclusion: Machine learning can be used to predict dose-volume parameter such as DDI in radiation treatment planning QA. Selection of a suitable machine learning algorithm is important to determine the parameter effectively.

Development of a modular system for estimating attenuation coefficient of gamma radiation: comparative study of different learning algorithms of cascade feed-forward neural network

In this paper, a modular system is developed for estimation of mass attenuation coefficient (MAC) of different materials/energies using artificial neural network (ANN). Cascade feed-forward neural network (CFFNN) as a type of ANN constructs mapping function between input patterns and the targets (i.e. MAC). Performance of different learning algorithms of CFFNN including gradient descent (GD), gradient descent with momentum (GDM), scaled conjugate gradient (SCG), Levenberg-Marquardt (LM), and Bayesian regularization (BR) are compared. For training, different categories of input patterns features are utilized to show the more appropriate one. Average mean relative error (AMRE) and cumulative distribution function (CDF) of the results indicate that BR learning algorithm accompany with the selected category of features (i.e. Z, E, and ρ) is more accurate for estimation of MAC (e.g. CDFAl (0.0069) = 0.99 and AMREAl = 0.0017). The advantages of the present method are: 1- Estimation of MAC is done fast (i.e. in comparison with Monte Carlo methods) and is done at a lower cost (i.e. without need to extra experiments) 2- Modular system reduces the risk of incorrect estimation 3- It is possible to extend the number of estimators for more materials/mixtures without unfavorably affecting the existing system.

An adaptive sampling strategy for quasi real time crack characterization on eddy current testing signals

This work describes quasi real time flaw(s) characterization in conductive plate(s) through the inversion of eddy current testing (ECT) signals using learning by examples (LBE) paradigm. Within the framework of LBE, a fast and accurate learning model is fitted on an optimal training set based on simulated eddy current testing data and the corresponding set of parameters during a preliminary offline phase. More specially, the optimal training set has been generated in the offline phase by adopting an adaptive sampling strategy through exploiting Partial Least Squares (PLS) feature extraction and output space filling (OSF). Subsequently, a non linear model is fitted on the training set and used to predict the set of parameters associated to an unknown (possibly large) test set during the so-called online phase. Different models, i.e., learning algorithms, such as Support Vector Regression (SVR), Kernel Ridge Regression (KRR), Relevance Vector Regression (RVR) and Augmented Radial Basis Function (A-RBF) have been adopted in order to build different accurate predictors. Afterwards, quasi real-time inversion has been performed on unknown test set by utilizing the corresponding trained models. Comparative results are reported through numerical and experimental data sets to assess the inversion performance of different learning algorithms based on the PLS-OSF sampling strategy.

Sentiment Identification in Football-Specific Tweets

Sports fans generate a large amount of tweets which reflect their opinions and feelings about what is happening during various sporting events. Given the popularity of football events, in this work, we focus on analyzing sentiment expressed by football fans through Twitter. These tweets reflect the changes in the fans’ sentiment as they watch the game and react to the events of the game, e.g., goal scoring, penalties, and so on. Collecting and examining the sentiment conveyed through these tweets will help to draw a complete picture which expresses fan interaction during a specific football event. The objective of this work is to propose a domain-specific approach for understanding sentiments expressed in football fans’ conversations. To achieve our goal, we start by developing a football-specific sentiment dataset which we label manually. We then utilize our dataset to automatically create a football-specific sentiment lexicon. Finally, we develop a sentiment classifier which is capable of recognizing sentiments expressed in football conversation. We conduct extensive experiments on our dataset to compare the performance of different learning algorithms in identifying the sentiment expressed in football related tweets. Our results show that our approach is effective in recognizing the fans’ sentiment during football events.

Willing humility: Negative capability and narcissism in psychoanalysis

It is common knowledge that the performance of different learning algorithms depends on certain characteristics of the data—such as dimensionality, linear separability or sample size. However, formally investigating this relationship in an objective and reproducible way is not trivial. A new formal framework for describing the relationship between data set characteristics and the performance of different learning algorithms is proposed. The framework combines the advantages of benchmark experiments with the formal description of data set characteristics by means of statistical and information-theoretic measures and with the recursive partitioning of Bradley–Terry models for comparing the algorithms’ performances. The formal aspects of each component are introduced and illustrated by means of an artificial example. Its real-world usage is demonstrated with an application example consisting of thirteen widely-used data sets and six common learning algorithms. The Appendix provides information on the implementation and the usage of the framework within the R language.

Discrete representation strategies for foreign exchange prediction

This is an extended version of the paper presented at the 4th International Workshop NFMCP 2015 held in conjunction with ECML PKDD 2015. The initial version has been published in NFMCP 2015 conference proceedings as part of Springer Series. This paper presents a novel approach to financial times series (FTS) prediction by mapping hourly foreign exchange data to string representations and deriving simple trading strategies from them. To measure the degree of similarity in these market strings we apply familiar string kernels, bag of words and n-grams, whilst also introducing a new kernel, time-decay n-grams, that captures the temporal nature of FTS. In the process we propose a sequential Parzen windows algorithm based on discrete representations where trading decisions for each string are learned in an online manner and are thus subject to temporal fluctuations. We evaluate the strength of a number of representations using both the string version and its continuous counterpart, whilst also comparing the performance of different learning algorithms on these representations, namely support vector machines, Parzen windows and Fisher discriminant analysis. Our extensive experiments show that the simple string representation coupled with the sequential Parzen windows approach is capable of outperforming other more exotic approaches, supporting the idea that when it comes to working in high noise environments often the simplest approach is the most effective.

Detecting Predatory Behavior in Game Chats

While games are a popular social media for children, there is a real risk that these children are exposed to potential sexual assault. A number of studies have already addressed this issue, however, the data used in previous research did not properly represent the real chats found in multiplayer online games. To address this issue, we obtained real chat data from MovieStarPlanet, a massively multiplayer online game for children. The research described in this paper aimed to detect predatory behaviors in the chats using machine learning methods. In order to achieve a high accuracy on this task, extensive preprocessing was necessary. We describe three different strategies for data selection and preprocessing, and extensively compare the performance of different learning algorithms on the different data sets and features.

(Psycho-)analysis of benchmark experiments: A formal framework for investigating the relationship between data sets and learning algorithms

It is common knowledge that the performance of different learning algorithms depends on certain characteristics of the data—such as dimensionality, linear separability or sample size. However, formally investigating this relationship in an objective and reproducible way is not trivial. A new formal framework for describing the relationship between data set characteristics and the performance of different learning algorithms is proposed. The framework combines the advantages of benchmark experiments with the formal description of data set characteristics by means of statistical and information-theoretic measures and with the recursive partitioning of Bradley–Terry models for comparing the algorithms’ performances. The formal aspects of each component are introduced and illustrated by means of an artificial example. Its real-world usage is demonstrated with an application example consisting of thirteen widely-used data sets and six common learning algorithms. The Appendix provides information on the implementation and the usage of the framework within the R language.