A Structure Learning Algorithm for Bayesian Network Using Prior Knowledge

Learning structure from data is one of the most important fundamental tasks of Bayesian network research. Particularly, learning optional structure of Bayesian network is a non-deterministic polynomial...     

PAC学习模型研究

计算学习理论为比较两算法的性能提供了形式化的框架，并能确定某概念类的计算复杂度和样本复杂度。而PAC学习模型是计算学习理论的基础，它为研究学习及泛化问题提供了一种基本的概率框架：先介绍了基本的PVC学习模型并对其进行了深入的分析，给出了判断一概念类是否是PAC学习的方法；然后针对基本PAC学习模型的不足进行了相应的扩展；最后介绍了计算学习理论中的一些其它模型。     

PAC Learning of One-Dimensional Patterns

Developing the ability to recognize a landmark from a visual image of a robot's current location is a fundamental problem in robotics. We consider the problem of PAC-learning the concept class of geometric patterns where the target geometric pattern is a configuration ofk points on the real line. Each instance is a configuration ofn points on the real line, where it is labeled according to whether or not it visually resembles the target pattern. To capture the notion of visual resemblance we use the Hausdorff metric. Informally, two geometric patternsP andQ resemble each other under the Hausdorff metric if every point on one pattern is close to some point on the other pattern. We relate the concept class of geometric patterns to the landmark matching problem and then present a polynomial-time algorithm that PAC-learns the class of one-dimensional geometric patterns. We also present some experimental results on how our algorithm performs.     

机器人修磨中融合先验知识的适应学习建模方法

针对机器人修磨磨削量建模中处理突变因素的难题,本文首先从机器学习建模方法的角度指出该问题与统计学习的不同点,并把问题形式化,然后在此基础上提出了融合先验知识的适应学习建模方法.该方法基于半经验公式生成虚拟样本,不但弥补了适应学习建模中新样本不足的问题,而且把半经验公式中的信息更充分地融合到学习机模型中.实验结果证明,该...     

基于先验形状约束水平集模型的建筑物提取方法

提出了一种先验形状约束的变分水平集模型, 并将其用于单幅遥感图像多建筑物的自动提取中. 将多个先验形状竞争模型引入水平集方法中, 在标记函数的指导下, 利用先验形状能量来约束曲线的演化, 在对图像进行分割的同时完成建筑物的检测和提取. 标记函数的引入, 加强了先验形状与要检测目标之间的匹配关系. 同时本文提出的模型具有先验形状的旋转、缩放和平移不变性. 最后的实验结果及定量定性的分析说明了本文方法的可行性.     

Incorporating Prior Knowledge in the Form of Production Rules into Neural Networks Using Boolean-Like Neurons

At present, nearly all neural networks are formulated by learning only from examples or patterns. For a real-word problem, some forms of prior knowledge in a non-example form always exist. Incorporation of prior knowledge will benefit the formulation of neural networks. Prior knowledge could be in several forms. Production rule is one form in which the prior knowledge is frequently represented. This paper proposes an approach to incorporate production rules into neural networks. A newly defined neuron architecture, Boolean-like neuron, is proposed. With this Boolean-like neuron, production rules can be encoded into the neural network during the network initialization period. Experiments are described in this paper. The results show that the incorporation of this prior knowledge can not only increase the training speed, but also the explainability of the neural networks.     

语言先验知识对神经网络模型自然语言处理任务的影响

随着互联网的发展及硬件的更新,神经网络模型被广泛应用于自然语言处理、图像识别等领域。目前,结合传统自然语言处理方法和神经网络模型正日益成为研究的热点。引入先验知识代表了传统方法的惯例,然而它们对基于神经网络模型的自然语言处理任务的影响尚不清楚。鉴于此,该文尝试探究语言层先验知识对基于神经网络模型的若干自然语言处理任务的影响。根据不同任务的特点,比较了不同先验知识和不同输入位置对不同神经网络模型的影响。通过大量的对比实验发现: 先验知识并不是对所有任务都适用,在神经网络模型的合适位置加入合适的先验知识方可加快模型的收敛速度,提高相关任务的效果。     

利用标签相关性先验的弱监督多标签学习方法

多标签学习是一种非常重要的机器学习范式.传统的多标签学习方法是在监督或半监督的情况下设计的.通常情况下,它们需要对所有或部分数据进行准确的属于多个类别的标注.在许多实际应用中,拥有大量标注的标签信息往往难以获取,限制了多标签学习的推广和应用.与之相比,标签相关性作为一种常见的弱监督信息,它对标注信息的要求较低.如何利用标签相关性进行多标签学习,是一个重要但未研究的问题.提出了一种利用标签相关性作为先验的弱监督多标签学习方法(WSMLLC).该模型利用标签相关性对样本相似性进行了重述,能够有效地获取标签指示矩阵;同时,利用先验信息对数据的投影矩阵进行约束,并引入回归项对指示矩阵进行修正.与现有方法相比,WSMLLC模型的突出优势在于:仅提供标签相关性先验,就可以实现多标签样本的标签指派任务.在多个公开数据集上进行实验验证,实验结果表明:在标签矩阵完全缺失的情况下,WSMLLC与当前先进的多标签学习方法相比具有明显优势.     

先验知识与基于核函数的回归方法的融合

In some sample based regression tasks, the observed samples are quite few or not informative enough. As a result, the conflict between the number of samples and the model complexity emerges, and the regression method will confront the dilemma whether to choose a complex model or not. Incorporating the prior knowledge is a potential solution for this dilemma. In this paper, a sort of the prior knowledge is investigated and a novel method to incorporate it into the kernel based regression scheme is proposed. The proposed prior knowledge based kernel regression (PKBKR) method includes two subproblems: representing the prior knowledge in the function space, and combining this representation and the training samples to obtain the regression function. A greedy algorithm for the representing step and a weighted loss function for the incorporation step are proposed. Finally, experiments are performed to validate the proposed PKBKR method, wherein the results show that the proposed method can achieve relatively high regression performance with appropriate model complexity, especially when the number of samples is small or the observation noise is large.     

一种先验知识引导的基于二阶段渐进网络的自主抓取策略

针对未知不规则物体在堆叠场景下的抓取任务,提出一种基于二阶段渐进网络(two-stage progressive network,TSPN)的自主抓取方法.首先利用端对端策略获取全局可抓性分布,然后基于采样评估策略确定最优抓取配置.将以上2种策略融合,使得TSPN的结构更加精简,显著减少了需评估样本的数量,能够在保证泛化能力的同时提升抓取效率.为了加快抓取模型学习进程,引入一种先验知识引导的自监督学习策略,并利用220种不规则物体进行抓取学习.在仿真和真实环境下分别进行实验,结果表明该抓取模型适用于多物体、堆叠物体、未知不规则物体、物体位姿随机等多种抓取场景,其抓取准确率和探测速度较其他基准方法有明显提升.整个学习过程历时10天,结果表明使用先验知识引导的学习策略能显著加快学习进程.     

PAC Learning Axis-aligned Rectangles with Respect to Product Distributions from Multiple-Instance Examples

We describe a polynomial-time algorithm for learning axis-aligned rectangles in Q ^d with respect to product distributions from multiple-instance examples in the PAC model. Here, each example consists of n elements of Q^d together with a label indicating whether any of the n points is in the rectangle to be learned. We assume that there is an unknown product distribution D over Q ^d such that all instances are independently drawn according to D. The accuracy of a hypothesis is measured by the probability that it would incorrectly predict whether one of n more points drawn from D was in the rectangle to be learned. Our algorithm achieves accuracy with probability 1- in O (d⁵ n¹²/²⁰ log² nd/ time.     

A Fixed-Distribution PAC Learning Theory for Neural FIR Models

The PAC learning theory creates a framework to assess the learning properties of static models. This theory has been extended to include learning of modeling tasks with m-dependent data given that the data are distributed according to a uniform distribution. The extended theory can be applied for learning of nonlinear FIR models with the restriction that the data are unformly distributed. In this paper, The PAC learning scheme is extended to deal with any FIR model regardless of the distribution of the data. This fixed-distribution m-dependent extension of the PAC learning theory is then applied to the learning of FIR three-layer feedforward sigmoid neural networks.     

引入统计先验的人脸图像恢复

将人脸形状和纹理的统计先验作为约束,引入经典正则化图像恢复算法框架,并给出迭代求解算法;同时,定义了反映图像模糊程度的边缘活动度,并在迭代的每一步中计算图像的边缘活动度,以确定在迭代求解过程中人脸先验对解进行约束的程度．由于人脸统计先验的约束以及引入边缘活动度来指导迭代求解过程,避免了由经典恢复算法得到的结果中会出现的振铃波纹．对实验结果的分析和主观感受表明：文中算法在恢复质量和抗噪能力方面均取得令人满意的结果．     

基于建构主义学习理论的个性化知识推荐模型

个性化推荐正成为“互联网+”和“大数据”时代信息网络服务的基本形式,虽然其已在电子商务和社交媒体的广泛应用中产生了巨大的商业价值,但在具有巨大潜在社会价值的个性化知识学习领域,相关研究与应用还较为稀少.研究提出一种基于建构主义学习理论的个性化知识推荐方法——建构推荐模型.新模型首先考虑将知识系统以知识网络的形式进行表达,随后引入最近邻优先的候选知识选择策略,以及基于最大可学习支撑度优先的top-K未学知识推荐算法.建构推荐模型通过知识网络的知识关联结构挖掘用户知识需求,并推荐给出最具建构学习价值的待学新知识.以饮食健康知识系统学习为例的实验分析表明,新模型在多种情况下推荐产生的个性化知识序列均具有较强的知识关联性和较高的知识体系覆盖率.     

#br#耦合样本先验分布信息的加权极限学习机

极限学习机广泛用于分类、聚类、回归等任务中,但在处理类不平衡分类问题时,前人未充分考虑样本先验分布信息对分类性能的影响。针对此问题,本文提出耦合样本先验分布信息的加权极限学习机（Coupling sample Prior distribution Weighted Extreme Learning Machine,CPWELM）算法。该算法基于加权极限学习机,充分探讨不同分布样本点的重要程度,以此构造代价矩阵,进而提升分类器性能。本文通过12个不平衡数据集,对CPWELM算法的可行性及有效性进行了验证。结果表明,相比同类其他算法,CPWELM算法的性能更优。     

Hough变换与先验知识在车牌定位中的新应用

车牌定位作为车牌识别系统中的基础环节,定位准确度将直接影响着最终识别结果。在以往各种识别算法中,Hough变换更多的是用来检测车牌倾斜角度,在车牌定位中一直未见其用。结合合理的先验知识,提出一种利用Hough变换多线检测在车牌定位中的新用法。通过对240幅图像的测试,准确率达到95.42%,结果表明了此方法的有效性。     

基于LOM的知识模型与个性化学习控制

在智能教学系统中,知识模型是专家模型的核心,是实现智能教学的一个关键问题。本文讨论知识点及其相互关系的形式表示,提出基于LOM的知识点模型与学习控制策略。知识点被设计、封装为学习对象,提高了学习内容的可重用性、可管理性、可定制性与互操作性。LOM的语义描述能力,使得系统本身具有一定程度的知识理解能力。以知识点LOM模型为结点的知识网络是语义网络,其语义计算能力便于知识的发现与关联。将专家知识网络动态映射为学生知识网络,实现了个性化学习。     

强化学习中基于定性模型的知识传递方法

本文提出一种基于定性模糊网络的强化学习知识传递方法。该方法通过建立系统的定性模型,并用定性模糊网络抽取基于定性动作的次优策略的共同特征获得与系统参数无关知识。这些知识能有效描述参数值不同的系统所具有的共同控制规律,加快在新参数值的系统中强化学习的收敛速度。     

PAC Learning Intersections of Halfspaces with Membership Queries

A randomized learning algorithm {POLLY} is presented that efficiently learns intersections of s halfspaces in n dimensions, in time polynomial in both s and n . The learning protocol is the PAC (probably approximately correct) model of Valiant, augmented with membership queries. In particular, {POLLY} receives a set S of m = poly(n,s,1/ε,1/δ) randomly generated points from an arbitrary distribution over the unit hypercube, and is told exactly which points are contained in, and which points are not contained in, the convex polyhedron P defined by the halfspaces. {POLLY} may also obtain the same information about points of its own choosing. It is shown that after poly(n , s , 1/ε , 1/δ , log(1/d) ) time, the probability that {POLLY} fails to output a collection of s halfspaces with classification error at most ε , is at most δ . Here, d is the minimum distance between the boundary of the target and those examples in S that are not lying on the boundary. The parameter log(1/d) can be bounded by the number of bits needed to encode the coefficients of the bounding hyperplanes and the coordinates of the sampled examples S . Moreover, {POLLY} can be extended to learn unions of k disjoint polyhedra with each polyhedron having at most s facets, in time poly(n , k , s , 1/ε , 1/δ , log(1/d) , 1/γ ) where γ is the minimum distance between any two distinct polyhedra. Received February 5, 1997; revised July 1, 1997.