Improving page prefetching with prior knowledge

Prefetching is a technique applied to memory management policies in which pages are brought into memory before they are actually needed. In this study, prior knowledge of program behavior obtained through trace data is used to parameterize a variation of the working set memory management policy supporting demand page prefetching using one page lookahead. Two new algorithms supporting double page prefetching are proposed. A comparative analysis of them is made.Evaluation of the techniques through trace driven simulation shows that, in general, they are very effective in reducing the page fault rate and in some cases the space time product. As expected, memory occupancy increases, but usually by small amounts. The results are encouraging for their ability to improve performance for a variety of programs which exhibit some form of locality (spatial and/or temporal).     

Robust adaptive regulation with minimal prior knowledge

A robust adaptive regulator is designed under minimal prior knowledge, namely, the order of a nominal plant model (in a well defined sense). The involved robustness means that the closed-loop stability is guaranteed in the presence of some class of time-varying parameters and unmodeled dynamics. The main design feature of the proposed adaptive regulator consists in its self-excitation capability together with an appropriate identification-stabilization time splitting. Unlike in the available literature concerning robust adaptive control, the involved self-excitation is established independently of the parameter estimator properties. The use of the robustness-oriented modifications as deadzone and parameter projection or contraction, which are aimed at achieving the parameter estimates boundedness, is no longer required beforehand, thereby reducing the underlying prior knowledge     

Induction of descriptive fuzzy classifiers with the Logitboost algorithm

Recently, Adaboost has been compared to greedy backfitting of extended additive models in logistic regression problems, or “Logitboost". The Adaboost algorithm has been applied to learn fuzzy rules in classification problems, and other backfitting algorithms to learn fuzzy rules in modeling problems but, up to our knowledge, there are not previous works that extend the Logitboost algorithm to learn fuzzy rules in classification problems.In this work, Logitboost is applied to learn fuzzy rules in classification problems, and its results are compared with that of Adaboost and other fuzzy rule learning algorithms. Contradicting the expected results, it is shown that the basic extension of the backfitting algorithm to learn classification rules may produce worse results than Adaboost does. We suggest that this is caused by the stricter requirements that Logitboost demands to the weak learners, which are not fulfilled by fuzzy rules. Finally, it is proposed a prefitting based modification of the Logitboost algorithm that avoids this problem     

Adapting prior knowledge activation: Mobilisation, perspective taking, and learners’ prior knowledge

This study investigates the effects of two prior knowledge activation strategies, namely, mobilisation and perspective taking, on learning. It is hypothesised that the effectiveness of these strategies is influenced by learners’ prior domain knowledge. More specifically, mobilisation is expected to be the most effective activation strategy at lower levels of prior knowledge. Mobilisation is a bottom-up oriented strategy that serves a broad stage-setting function. It provides learners with a relevant context in which new information can be integrated, which might be especially beneficial for learners with lower levels of prior knowledge to help them extend their limited knowledge base. As prior knowledge increases, perspective taking is expected to become the most effective strategy for activating learners’ prior knowledge. Perspective taking is a top-down oriented strategy that results in the activation of a corresponding schema. This schema guides the selection and processing of information relevant to the schema, which might especially support learners with higher levels of prior knowledge to refine their already elaborated knowledge base. The effectiveness of the activation strategies (in terms of learning task performance) was indeed influenced by learners’ prior knowledge in the hypothesised direction.     

Colorization by classifying the prior knowledge

When a one-dimensional luminance scalar is replaced by a vector of a colorful multi-dimension for every pixel of a monochrome image,the process is called colorization.However,colorization is under-constrained.Therefore,the prior knowledge is considered and given to the monochrome image.Colorization using optimization algorithm is an effective algorithm for the above problem.However,it cannot effectively do with some images well without repeating experiments for confirming the place of scribbles.In this paper,a colorization algorithm is proposed,which can automatically generate the prior knowledge.The idea is that firstly,the prior knowledge crystallizes into some points of the prior knowledge which is automatically extracted by downsampling and upsampling method.And then some points of the prior knowledge are classified and given with corresponding colors.Lastly,the color image can be obtained by the color points of the prior knowledge.It is demonstrated that the proposal can not only effectively generate the prior knowledge but also colorize the monochrome image according to requirements of user with some experiments.     

Accurate on-line support vector regression incorporated with compensated prior knowledge

Neural Computing and Applications - When the training data required by the data-driven model is insufficient or difficult to cover the sample space completely, incorporating the prior knowledge and...     

Imprecise prior knowledge incorporating into one-class classification

An extension of Campbell and Bennett’s novelty detection or one-class classification model incorporating prior knowledge is studied in the paper. The proposed extension relaxes the strong assumption of the empirical probability distribution over elements of a training set and deals with a set of probability distributions produced by prior knowledge about training data. The classification problem is solved by considering extreme points of the probability distribution set or by means of the conjugate duality technique. Special cases of prior knowledge are considered in detail, including the imprecise linear-vacuous mixture model and interval-valued moments of feature values. Numerical experiments show that the proposed models outperform Campbell and Bennett’s model for many real and synthetic data.     

Incorporating prior knowledge in fuzzy model identification

This paper presents an algorithm for incorporating a priori knowledge into data-driven identification of dynamic fuzzy models of the Takagi-Sugeno type. Knowledge about the modelled process such as its stability, minimal or maximal static gain, or the settling time of its step response can be translated into inequality constraints on the consequent parameters. By using input-output data, optimal parameter values are then found by means of quadratic programming. The proposed approach has been applied to the identification of a laboratory liquid level process. The obtained fuzzy model has been used in model-based predictive control. Real-time control results show that, when the proposed identification algorithm is applied, not only are physically justified models obtained but also the performance of the model-based controller improves with regard to the case where no prior knowledge is involved.     

Incorporating prior knowledge in support vector regression

This paper explores the incorporation of prior knowledge in support vector regresion by the addition of constraints. Equality and inequality constraints are studied with the corresponding types of prior knowledge that can be considered for the method. These include particular points with known values, prior knowledge on any derivative of the function either provided by a prior model or available only at some specific points and bounds on the function or any derivative in a given domain. Moreover, a new method for the simultaneous approximation of multiple outputs linked by some prior knowledge is proposed. This method also allows consideration of different types of prior knowledge on single outputs while training on multiple outputs. Synthetic examples show that incorporating a wide variety of prior knowledge becomes easy, as it leads to linear programs, and helps to improve the approximation in difficult cases. The benefits of the method are finally shown on a real-life application, the estimation of in-cylinder residual gas fraction in spark ignition engines, which is representative of numerous situations met in engineering. Editor: Dale Schuurmans.     

基于先验知识的目标平台识别方法

目标平台识别是雷达侦察任务中的一项重要内容。传统目标平台与辐射源识别结果关联方法未对目标配属辐射源的特征信息进行利用,存在着一定的模糊性。为此提出一种利用数据挖掘对辐射源与目标平台之间存在潜在关系进行挖掘的方法,并以此进行目标平台识别和可信度赋值。该方法实现简单,获得的结果可以直接参与多传感器融合的目标识别处理,具有较强的实用性。     

基于先验知识的自适应检测波形设计

研究了基于先验知识的认知雷达自适应检测波形设计问题,推导了发射波形对于检测性能影响的闭式解并提出了一种基于交替投影法的波形优化算法。算法根据所推导出的检测概率与发射波形、接收机滤波器之间关系的闭式表达式,通过联合优化发射波形和接收机滤波器,达到提高接收机的检测概率和降低信号相关杂波负面影响的目的。分析仿真结果可知,所设计波形的检测性能优于传统的恒包络LFM波形。当采用多组初始波形进行迭代优化时,检测概率趋向于相同值,这表明该算法的收敛性良好。     

Watershed segmentation using prior shape and appearance knowledge

Watershed transformation is a common technique for image segmentation. However, its use for automatic medical image segmentation has been limited particularly due to oversegmentation and sensitivity to noise. Employing prior shape knowledge has demonstrated robust improvements to medical image segmentation algorithms. We propose a novel method for enhancing watershed segmentation by utilizing prior shape and appearance knowledge. Our method iteratively aligns a shape histogram with the result of an improved k-means clustering algorithm of the watershed segments. Quantitative validation of magnetic resonance imaging segmentation results supports the robust nature of our method.     

Incorporating prior knowledge into learning by dividing training data

In most large-scale real-world pattern classification problems, there is always some explicit information besides given training data, namely prior knowledge, with which the training data are organized. In this paper, we proposed a framework for incorporating this kind of prior knowledge into the training of min-max modular (M³) classifier to improve learning performance. In order to evaluate the proposed method, we perform experiments on a large-scale Japanese patent classification problem and consider two kinds of prior knowledge included in patent documents: patent’s publishing date and the hierarchical structure of patent classification system. In the experiments, traditional support vector machine (SVM) and M³-SVM without prior knowledge are adopted as baseline classifiers. Experimental results demonstrate that the proposed method is superior to the baseline classifiers in terms of training cost and generalization accuracy. Moreover, M³-SVM with prior knowledge is found to be much more robust than traditional support vector machine to noisy dated patent samples, which is crucial for incremental learning.     

The impact of prior knowledge on causal structure learning

Knowledge and Information Systems - Causal Bayesian networks have become a powerful technology for reasoning under uncertainty in areas that require transparency and explainability, by relying on...     

A multi-objective model for discovering high-quality knowledge based on data quality and prior knowledge

Discovering knowledge from data means finding useful patterns in data, this process has increased the opportunity and challenge for businesses in the big data era. Meanwhile, improving the quality of the discovered knowledge is important for making correct decisions in an unpredictable environment. Various models have been developed in the past; however, few used both data quality and prior knowledge to control the quality of the discovery processes and results. In this paper, a multi-objective model of knowledge discovery in databases is developed, which aids the discovery process by utilizing prior process knowledge and different measures of data quality. To illustrate the model, association rule mining is considered and formulated as a multi-objective problem that takes into account data quality measures and prior process knowledge instead of a single objective problem. Measures such as confidence, support, comprehensibility and interestingness are used. A Pareto-based integrated multi-objective Artificial Bee Colony (IMOABC) algorithm is developed to solve the problem. Using well-known and publicly available databases, experiments are carried out to compare the performance of IMOABC with NSGA-II, MOPSO and Apriori algorithms, respectively. The computational results show that IMOABC outperforms NSGA-II, MOPSO and Apriori on different measures and it could be easily customized or tailored to be in line with user requirements and still generates high-quality association rules.     

Parameter learning in hybrid Bayesian networks using prior knowledge

Mixtures of truncated basis functions have been recently proposed as a generalisation of mixtures of truncated exponentials and mixtures of polynomials for modelling univariate and conditional distributions in hybrid Bayesian networks. In this paper we analyse the problem of learning the parameters of marginal and conditional MoTBF densities when both prior knowledge and data are available. Incorporating prior knowledge provide a valuable tool for obtaining useful models, especially in domains of applications where data are costly or scarce, and prior knowledge is available from practitioners. We explore scenarios where the prior knowledge can be expressed as an MoTBF density that is afterwards combined with another MoTBF density estimated from the available data. The resulting model remains within the MoTBF class which is a convenient property from the point of view of inference in hybrid Bayesian networks. The performance of the proposed method is tested in a series of experiments carried out over synthetic and real data.     

Adaptive switching supervisory control of nonlinear systems with no prior knowledge of noise bounds

The problem of controlling nonlinear noisy systems affected by parametric uncertainties is approached via the introduction of a supervisor which, whenever needed, switches on, in feedback to the plant, a controller selected from a finite set of predesigned controllers. A Lyapunov-based falsification criterion allows one to ensure robust stability in the presence of uncertain constant parameters and exogenous bounded disturbances. Simulations are discussed in order to illustrate the merits of the proposed algorithms.     

基于先验知识的非负矩阵半可解释三因子分解算法

非负矩阵三因子分解是潜在因子模型中的重要组成部分,由于能将原始数据矩阵分解为三个相互约束的潜因子矩阵,被广泛应用于推荐系统、迁移学习等研究领域,但目前还没有非负矩阵三因子分解的可解释性方面的研究工作.鉴于此,将用户评论文本信息当作先验知识,设计了一种基于先验知识的非负矩阵半可解释三因子分解(PE-NMTF)算法.首先利...     

基于先验信息和谱分析的聚类融合算法

在聚类过程中利用先验信息能显著提高聚类算法的性能,但已存在的聚类融合算法很少考虑到数据集的先验信息。基于先验信息和谱分析,提出一种聚类融合算法,将成对限制信息引入到谱聚类算法中,用受限的谱聚类算法产生聚类成员,再采用基于互联合矩阵的集成方法生成最后的聚类结果。实验结果表明,利用先验信息能有效提高聚类的效果。