nils-herrmann/clean_scirepeval huggingface dataset:
Extract rules from software quality prediction model based on neural network

To get a highly reliable software product to the market on schedule, software engineers must allocate resources on the fault-prone software modules across the development effort. Software quality models based upon data mining from past projects can identify fault-prone modules in current similar development efforts. So that resources can be focused on fault-prone modules to improve quality prior to release. Many researchers have applied the neural networks approach to predict software quality. Although neural networks have shown their strengths in solving complex problems, their shortcoming of being 'black boxes' models has prevented them from being accepted as a common practice for fault-prone software modules prediction. That is a significant weakness, for without the ability to produce comprehensible decisions; it is hard to trust the reliability of neural networks that address real-world problems. We introduce an interpretable neural network model for software quality prediction. First, a three-layer feed-forward neural network with the sigmoid function in hidden units and the identity function in output unit was trained. The data used to train the neural network is collected from an earlier release of a telecommunications software system. Then use clustering genetic algorithm (CCA) to extract comprehensible rules from the trained neural network. We use the rule set extracted from the trained neural network to detect the fault-prone software modules of the later release and compare the predicting results with the neural network predicting results. The comparison shows that although the rule set's predicting accuracy is a little less than the trained neural network, it is more comprehensible.
The structure and its implementation of hidden dynamic HMM for Mandarin speech recognition

A large hydraulic excavator machine for mining operations comprises attachment means for operatively connecting the shovel to the machine frame so that no bending moment is imposed on the component parts of the attachment means during operation of the excavator. The attachment means comprises components including a stroke arm movable by an extendable and retractable hydraulic stroke arm cylinder; and a shovel arm assembly on which the bucket is mounted and which is movable by an extendable and retractable hydraulic shovel arm cylinder. The stroke arm and stroke arm cylinder are pivotally connected at their lower ends to spaced apart points on the excavator frame and are pivotally connected at their upper ends to a common point. The upper end of the shovel arm assembly is also connected to the common point. The shovel arm cylinder has one end pivotally connected to the machine frame (or to an extension therefrom) and has its other end pivotally connected to the shovel arm assembly. The lattice-like arrangement of the components and the location of the points of attack of the stroke arm cylinder and the shovel arm cylinder prevent bending moments from being imposed on the components.
On use of duration modeling for continuous digits speech recognition

In this paper, we describe our duration model techniques in HMM based speech recognizer. With this approach, a large amount of deletion and insertion errors can be reduced in Mandarin continuous digits recognizer. We address a simple duration penalty function, which can be explicitly combined into Viterbi-Beam search with negligible incremental computation overload. Different parametric distributions are investigated to accurately approximate the syllable-level duration information. A relative Rate of Speech (ROS) based duration normalization scheme is proposed to eliminate variation caused by different speaking rate. In order to directly incorporate this normalization strategy, an online dynamic ROS estimation method is introduced into real-time recognition application. Experimental results demonstrated significant performance improvement has been achieved. The word error rate (WER) was reduced 52.1%, compared with our baseline recognition system.
The analysis and application of a new endpoint detection method based on distance of autocorrelated similarity#

Endpoints detection play the important role in speech recognition. But the performance of some often used algorithms will decrease under low SNR conditions. A new concept as distance between autocorrelated functions is brought fully, and a new endpoint detecting method based on it is discussed in this paper. Results from experiment show that this new method has higher performance in endpoint detection. Especially in low SNR circumstance, it still can detect endpoint of speech accurately.
Training Support Vector Machines via SMO-Type Decomposition Methods

This article gives a comprehensive study on SMO-type (Sequential Minimal Optimization) decomposition methods for training support vector machines. We propose a general and flexible selection of the two-element working set. Main theoretical results include 1) a simple asymptotic convergence proof, 2) a useful explanation of the shrinking and caching techniques, and 3) the linear convergence of this method. This analysis applies to any SMO-type implementation whose selection falls into the proposed framework.
A tutorial on?-support vector machines

Disclosed are apparatus (10) for making a complexly patterned extrudate. The apparatus (10) includes a food cooker extruder (12) for providing at least one extrudable food product, at least one food color supply (18), a pattern forming die (20) for mixing the food color and extrudate food product to form a complexly patterned food product, a reducing passageway for reducing the cross sectional area of the patterned dough from an inlet end (34) to outlet end (36) of at least 50:1 at an average convergence angle of </=45 DEG and an extruder exit port at the discharge end of the reducing passageway. The methods comprise the steps of: providing a plastic extrudable food mass; providing at least one color; mixing the food mass and color to form an organized complexly patterned food dough having an initial cross sectional area; reducing the initial cross sectional area by a factor of at least 50:1 with an average convergence angle of </=45 DEG while maintaining the cross sectional pattern to form a reduced cross sectional patterned dough, and extruding the reduced cross sectional dough through a die port having an opening of equal to the reduced cross sectional area to form a complexly patterned extrudate.
Multi-label Classification by Pariwise Training

This paper presents a simple and general approach for multi-label problems. In contrast with the commonly used binary approach, which roots from the 1-vs-rest strategy for multi-class classification, the new method extends the 1-vs-1 strategy and conducts pairwise training. While the binary approach assumes labels are assigned independently, the proposed method better incorporates information between any two labels. Behind this new framework any classification technique can be used as the base for training/predicting two-class sub-problems, but here we focus on using support vector machines (SVM). Experiments on real-world sets show that the new approach is usually better than the binary approach.
Leave-One-Out Bounds for Support Vector Regression Model Selection

Minimizing bounds of leave-one-out errors is an important and efficient approach for support vector machine (SVM) model selection. Past research focuses on their use for classification but not regression. In this letter, we derive various leave-one-out bounds for support vector regression (SVR) and discuss the difference from those for classification. Experiments demonstrate that the proposed bounds are competitive with Bayesian SVR for parameter selection. We also discuss the differentiability of leave-one-out bounds.
Analysis of switching dynamics with competing support vector machines

We present a framework for the unsupervised segmentation of switching dynamics using support vector machines. Following the architecture by Pawelzik et al., where annealed competing neural networks were used to segment a nonstationary time series, in this paper, we exploit the use of support vector machines, a well-known learning technique. First, a new formulation of support vector regression is proposed. Second, an expectation-maximization step is suggested to adaptively adjust the annealing parameter. Results indicate that the proposed approach is promising.
Simple Probabilistic Predictions for Support Vector Regression

Support vector regression (SVR) has been popular in the past decade, but it provides only an estimated target value instead of predictive probability intervals. Many work have addressed this issue but sometimes the SVR formula must be modified. This paper presents a rather simple and direct approach to construct such intervals. We assume that the conditional distribution of the target value depends on its input only through the predicted value, and propose to model this distribution by simple functions. Experiments show that the proposed approach gives predictive intervals with competitive coverages with Bayesian SVR methods.  In the past decade support vector regression (SVR) ,  has been popular for regression problems. SVR provides only an estimated target value; however, the statement that the future value falls in an interval with a specified probability is more informative. This paper aims to construct predictive intervals for the future values. For conventional linear regression, the prediction interval has been well developed; for example, see [16] for Gaussian noise case and ,  for non-Gaussian case. SVR differs from conventional regression in that it maps input data into a high dimensional reproducing kernel Hilbert space and uses an -insensitive loss function. As a result, SVR has a sparse representation of solutions, and hence is relatively fast in training/testing. However, due to these differences, the existing methods for constructing prediction intervals can not be applied. Recently Bayesian interpretations of SVR have been developed [6], ,  along the ways of Bayesian techniques for Neural Networks [8] and for SVM classification [13], [11]. Using a Bayesian framework, one can determine parameters in SVR by maximizing an evidence function, and at the same time derive an error bar for prediction. Some of these Bayesian approaches perform well, but in several situations they cannot be applied. For example, they may modify the SVR formulation, so it is more difficult to use existing SVR software. In addition, some may prefer using other methods (e.g., cross validation) for parameter selection. As the best parameters are not from
Load forecasting using support vector Machines: a study on EUNITE competition 2001

Load forecasting is usually made by constructing models on relative information, such as climate and previous load demand data. In 2001, EUNITE network organized a competition aiming at mid-term load forecasting (predicting daily maximum load of the next 31 days). During the competition we proposed a support vector machine (SVM) model, which was the winning entry, to solve the problem. In this paper, we discuss in detail how SVM, a new learning technique, is successfully applied to load forecasting. In addition, motivated by the competition results and the approaches by other participants, more experiments and deeper analyses are conducted and presented here. Some important conclusions from the results are that temperature (or other types of climate information) might not be useful in such a mid-term load forecasting problem and that the introduction of time-series concept may improve the forecasting.
Decomposition Methods for Linear Support Vector Machines

In this letter, we show that decomposition methods with alpha seeding are extremely useful for solving a sequence of linear support vector machines (SVMs) with more data than attributes. This strategy is motivated by Keerthi and Lin (2003), who proved that for an SVM with data not linearly separable, after C is large enough, the dual solutions have the same free and bounded components. We explain why a direct use of decomposition methods for linear SVMs is sometimes very slow and then analyze why alpha seeding is much more effective for linear than nonlinear SVMs. We also conduct comparisons with other methods that are efficient for linear SVMs and demonstrate the effectiveness of alpha seeding techniques in model selection.
A Simple Decomposition Method for Support Vector Machines

The decomposition method is currently one of the major methods for solving support vector machines. An important issue of this method is the selection of working sets. In this paper through the design of decomposition methods for bound-constrained SVM formulations we demonstrate that the working set selection is not a trivial task. Then from the experimental analysis we propose a simple selection of the working set which leads to faster convergences for difficult cases. Numerical experiments on different types of problems are conducted to demonstrate the viability of the proposed method.
Decomposition methods for linear support vector machines

We explain that decomposition methods, in particular, SMO-type algorithms, are not suitable for linear SVMs with more data than attributes. To remedy this difficulty, we consider a recent result by S.S. Keerthi and C.-J. Lin (see http://www.csie.ntu.edu.tw//spl sim/cjlin/papers/limit.ps.gz, 2002) that for an SVM which is not linearly separable, after C is large enough, the dual solutions are at similar faces. Motivated by this property, we show that alpha seeding is extremely useful for solving a sequence of linear SVMs. It largely reduces the number of decomposition iterations to the point that solving many linear SVMs requires less time than the original decomposition method for one single SVM. We also conduct comparisons with other methods which are efficient for linear SVMs, and demonstrate the effectiveness of the proposed approach for helping the model selection.
Probability Estimates for Multi-class Classification by Pairwise Coupling

Pairwise coupling is a popular multi-class classification method that combines all comparisons for each pair of classes. This paper presents two approaches for obtaining class probabilities. Both methods can be reduced to linear systems and are easy to implement. We show conceptually and experimentally that the proposed approaches are more stable than the two existing popular methods: voting and the method by Hastie and Tibshirani (1998)
A study on reduced support vector machines

Recently the reduced support vector machine (RSVM) was proposed as an alternate of the standard SVM. Motivated by resolving the difficulty on handling large data sets using SVM with nonlinear kernels, it preselects a subset of data as support vectors and solves a smaller optimization problem. However, several issues of its practical use have not been fully discussed yet. For example, we do not know if it possesses comparable generalization ability as the standard SVM. In addition, we would like to see for how large problems RSVM outperforms SVM on training time. In this paper we show that the RSVM formulation is already in a form of linear SVM and discuss four RSVM implementations. Experiments indicate that in general the test accuracy of RSVM are a little lower than that of the standard SVM. In addition, for problems with up to tens of thousands of data, if the percentage of support vectors is not high, existing implementations for SVM is quite competitive on the training time. Thus, from this empirical study, RSVM will be mainly useful for either larger problems or those with many support vectors. Experiments in this paper also serve as comparisons of: 1) different implementations for linear SVM and 2) standard SVM using linear and quadratic cost functions.
Asymptotic Behaviors of Support Vector Machines with

Support vector machines (SVMs) with the gaussian (RBF) kernel have been popular for practical use. Model selection in this class of SVMs involves two hyperparameters: the penalty parameter C and the kernel width σ . This letter analyzes the behavior of the SVM classifier when these hyperparameters take very small or very large values. Our results help in understanding the hyperparameter space that leads to an efficient heuristic method of searching for hyperparameter values with small generalization errors. The analysis also indicates that if complete model selection using the gaussian kernel has been conducted, there is no need to consider linear SVM.
Radius Margin Bounds for Support Vector Machines with the RBF Kernel

An important approach for efficient support vector machine (SVM) model selection is to use differentiable bounds of the leave-one-out (loo) error. Past efforts focused on finding tight bounds of loo (e.g., radius margin bounds, span bounds). However, their practical viability is still not very satisfactory. Duan, Keerthi, and Poo (2003) showed that radius margin bound gives good prediction for L2-SVM, one of the cases we look at. In this letter, through analyses about why this bound performs well for L2-SVM, we show that finding a bound whose minima are in a region with small loo values may be more important than its tightness. Based on this principle, we propose modified radius margin bounds for L1-SVM (the other case) where the original bound is applicable only to the hard-margin case. Our modification for L1-SVM achieves comparable performance to L2-SVM. To study whether L1-or L2-SVM should be used, we analyze other properties, such as their differentiability, number of support vectors, and number of free support vectors. In this aspect, L1-SVM possesses the advantage of having fewer support vectors. Their implementations are also different, so we discuss related issues in detail.
Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel

Support vector machines (SVMs) with the gaussian (RBF) kernel have been popular for practical use. Model selection in this class of SVMs involves two hyper parameters: the penalty parameter C and the kernel width . This letter analyzes the behavior of the SVM classifier when these hyper parameters take very small or very large values. Our results help in understanding the hyperparameter space that leads to an efficient heuristic method of searching for hyperparameter values with small generalization errors. The analysis also indicates that if complete model selection using the gaussian kernel has been conducted, there is no need to consider linear SVM.
Training nu-support vector regression: theory and algorithms.

We discuss the relation between epsilon-support vector regression (epsilon-SVR) and nu-support vector regression (nu-SVR). In particular, we focus on properties that are different from those of C-support vector classification (C-SVC) and nu-support vector classification (nu-SVC). We then discuss some issues that do not occur in the case of classification: the possible range of epsilon and the scaling of target values. A practical decomposition method for nu-SVR is implemented, and computational experiments are conducted. We show some interesting numerical observations specific to regression.
Analysis of Nonstationary Time Series Using Support Vector Machines

Time series from alternating dynamics have many important applications. In , the authors propose an approach to solve the drifting dynamics. Their method directly solves a non-convex optimization problem. In this paper, we propose a strategy which solves a sequence of convex optimization problems by using modified support vector regression. Experimental results showing its practical viability are presented and we also discuss the advantages and disadvantages of the proposed approach.
A Note on the Decomposition Methods for Support Vector Regression

The dual formulation of support vector regression involves two closely related sets of variables. When the decomposition method is used, many existing approaches use pairs of indices from these two sets as the working set. Basically, they select a base set first and then expand it so all indices are pairs. This makes the implementation different from that for support vector classification. In addition, a larger optimization subproblem has to be solved in each iteration. We provide theoretical proofs and conduct experiments to show that using the base set as the working set leads to similar convergence (number of iterations). Therefore, by using a smaller working set while keeping a similar number of iterations, the program can be simpler and more efficient.
Training v-Support Vector Regression: Theory and Algorithms

We discuss the relation between-support vector regression (-SVR) and v-support vector regression (v-SVR). In particular, we focus on properties that are different from those of C-support vector classification (C-SVC) andv-support vector classification (v-SVC). We then discuss some issues that do not occur in the case of classification: the possible range of and the scaling of target values. A practical decomposition method forv-SVR is implemented, and computational experiments are conducted. We show some interesting numerical observations specific to regression.
A comparison of methods for multiclass support vector machines

Support vector machines (SVMs) were originally designed for binary classification. How to effectively extend it for multiclass classification is still an ongoing research issue. Several methods have been proposed where typically we construct a multiclass classifier by combining several binary classifiers. Some authors also proposed methods that consider all classes at once. As it is computationally more expensive to solve multiclass problems, comparisons of these methods using large-scale problems have not been seriously conducted. Especially for methods solving multiclass SVM in one step, a much larger optimization problem is required so up to now experiments are limited to small data sets. In this paper we give decomposition implementations for two such "all-together" methods. We then compare their performance with three methods based on binary classifications: "one-against-all," "one-against-one," and directed acyclic graph SVM (DAGSVM). Our experiments indicate that the "one-against-one" and DAG methods are more suitable for practical use than the other methods. Results also show that for large problems methods by considering all data at once in general need fewer support vectors.
Adaptive deterministic annealing for two applications: competing SVR of switching dynamics and travelling salesman problems

A deterministic annealing approach has been proposed to clustering by Rose et al. (1990; 1992) based on the maximum entropy principle. They obtain the association probabilities at a given average variance. The corresponding Lagrange multiplier is inversely related to the "temperature" and is used to control the annealing process. We propose an adaptive annealing schedule where the Lagrange multiplier is treated as an unknown parameter and is estimated by an expectation-maximization step. This technique is applied to using support vector regression (SVR) or switching dynamics. We also give some preliminary results on traveling salesman problems (TSP).