谈C语言的学习

由于C语言的知识点很零碎,初学者存在着较大的困难,笔者从教学的实践出发,给出学习C语言的方法,帮助初学者解决学习中的一些问题,寻找到适合自己的学习途径。     

谈C语言的学习

由于C语言的知识点很零碎,初学者存在着较大的困难,笔者从教学的实践出发,给出学习C语言的方法,帮助初学者解决学习中的一些问题,寻找到适合自己的学习途径。     

编程技巧在C语言学习中的应用

在许多应用软件运行时都带有命令行参数,这些命令行参数在C语言编写的程序中也可以实现,灵活地运用命令行参数进行处理可以有效地提高程序的运行效率,收到事半功倍的效果。详细分析了C语言编程技巧在C语言学习中的应用。     

Tolerating Concept and Sampling Shift in Lazy Learning Using Prediction Error Context Switching

In their unmodified form, lazy-learning algorithms may have difficulty learning and tracking time-varying input/output function maps such as those that occur in concept shift. Extensions of these algorithms, such as Time-Windowed forgetting (TWF), can permit learning of time-varying mappings by deleting older exemplars, but have decreased classification accuracy when the input-space sampling distribution of the learning set is time-varying. Additionally, TWF suffers from lower asymptotic classification accuracy than equivalent non-forgetting algorithms when the input sampling distributions are stationary. Other shift-sensitive algorithms, such as Locally-Weighted forgetting (LWF) avoid the negative effects of time-varying sampling distributions, but still have lower asymptotic classification in non-varying cases. We introduce Prediction Error Context Switching (PECS) which allows lazy-learning algorithms to have good classification accuracy in conditions having a time-varying function mapping and input sampling distributions, while still maintaining their asymptotic classification accuracy in static tasks. PECS works by selecting and re-activating previously stored instances based on their most recent consistency record. The classification accuracy and active learning set sizes for the above algorithms are compared in a set of learning tasks that illustrate the differing time-varying conditions described above. The results show that the PECS algorithm has the best overall classification accuracy over these differing time-varying conditions, while still having asymptotic classification accuracy competitive with unmodified lazy-learners intended for static environments.     

C语言程序设计教学中的移动学习尝试

分析了C语言程序设计课程的特点以及移动学习的主要应用模式;结合目前高等学校教学的实际情况,设计了C语言程序设计课程的移动学习的实现方式并分析了需要注意的事项;探讨了移动学习在高等学校教学中的应用模式.实验证明,合理使用移动学习方式能够调动学生的学习积极性.     

基于兴趣的计算机语言学习方法探索

从设计一个娱乐性小游戏出发,阐述兴趣发展的各阶段对计算机语言学习的影响。教学实践证明,游戏既能把学习者导向到有目的、有意义的积极学习,又能为学习者的进一步学习提供动机。     

浅谈数字语言学习系统应该实现的功能

文章从一线教学需求的角度全面论述了数字语言学习系统应该具备的主要功能,并在自主学习、网络化考试、智能化评价和互动交流四个方面提出了新的看法。     

面向汉语的计算机辅助语音学习系统特征的研究

本文在分析了语言及语音学习和教学的重要性及特点的基础上,讨论了将语音处理技术应用于语言、语音的计算机辅助学习或教学中所涉及的多方面问题;并针对汉语语音的特点,研究了面向汉语学习的CALL系统所应具有的特征,及其在设计和实现时应遵循的原则;最后借助通用语音分析器′Speech Analyzer′进行了汉语语音学习的尝试。     

Learning to Parse Natural Language with Maximum Entropy Models

This paper presents a machine learning system for parsing natural language that learns from manually parsed example sentences, and parses unseen data at state-of-the-art accuracies. Its machine learning technology, based on the maximum entropy framework, is highly reusable and not specific to the parsing problem, while the linguistic hints that it uses to learn can be specified concisely. It therefore requires a minimal amount of human effort and linguistic knowledge for its construction. In practice, the running time of the parser on a test sentence is linear with respect to the sentence length. We also demonstrate that the parser can train from other domains without modification to the modeling framework or the linguistic hints it uses to learn. Furthermore, this paper shows that research into rescoring the top 20 parses returned by the parser might yield accuracies dramatically higher than the state-of-the-art.     

A Model to Disambiguate Natural Language Parses on the Basis of User Language Proficiency: Design and Evaluation

This paper discusses the design and evaluation of an implemented user model in ICICLE, an instruction system for users writing in a second language. We show that in the task of disambiguating natural language parses, a blended model combining overlay techniques with user stereotyping representing typical linguistic acquisition sequences captures user individuality while supplementing incomplete information with stereotypic reasoning     

浅谈数字语言学习系统应该实现的功能

文章从一线教学需求的角度全面论述了数字语言学习系统应该具备的主要功能,并在自主学习、网络化考试、智能化评价和互动交流四个方面提出了新的看法。     

MTL-BERT:一种结合BERT的中文文本多任务学习模型

单任务学习常常受限于单目标函数的不足,多任务学习能有效利用任务相关性的先验性,故而受到了学界的关注.在中文自然语言处理领域,关于多任务学习的研究极为匮乏,该领域需同时考虑到中文文本特征提取和多任务的建模.本论文提出了一种多任务学习模型MTL-BERT.首先将BERT作为特征提取器以提升模型的泛化性.其次分类和回归是机器...     

Comprehension Grammars Generated from Machine Learning of Natural Languages

We are developing a theory of probabilistic language learning in the context of robotic instruction in elementary assembly actions. We describe the process of machine learning in terms of the various events that happen on a given trial, including the crucial association of words with internal representations of their meaning. Of central importance in learning is the generalization from utterances to grammatical forms. Our system derives a comprehension grammar for a superset of a natural language from pairs of verbal stimuli like Go to the screw! and corresponding internal representations of coerced actions. For the derivation of a grammar no knowledge of the language to be learned is assumed but only knowledge of an internal language.We present grammars for English, Chinese, and German generated from a finite sample of about 500 commands that are roughly equivalent across the three languages. All of the three grammars, which are context-free in form, accept an infinite set of commands in the given language.     

Learning Information Extraction Rules for Semi-Structured and Free Text

A wealth of on-line text information can be made available to automatic processing by information extraction (IE) systems. Each IE application needs a separate set of rules tuned to the domain and writing style. WHISK helps to overcome this knowledge-engineering bottleneck by learning text extraction rules automatically.WHISK is designed to handle text styles ranging from highly structured to free text, including text that is neither rigidly formatted nor composed of grammatical sentences. Such semi-structured text has largely been beyond the scope of previous systems. When used in conjunction with a syntactic analyzer and semantic tagging, WHISK can also handle extraction from free text such as news stories.     

工学结合模式下的C语言教学

在高职高专院校工学结合模式下,对C语言教学提出了更高的要求,可以从教材的选择与课程安排、理论教学、实践教学以及对教师的要求4个方面进行教学改革,最终实现企业需求与"学"、"训"的紧密结合,培养出符合企业要求的高技能高素质人才。     

基于R语言的机器人教育微博可视化研究

本文利用R语言对关注度不高、转发量不太大的机器人教育微博进行了可视化研究。在分析技术实现过程的同时,我们得出结论是:机器人教育的舆情由教育官方媒体和企业培训机构主导;经济力量决定着参与讨论的程度;微博不是学习内容交流的主阵地。     

C语言编程技巧在C语言学习中的应用

C语言是功能强大、应用广泛的编程语言,也是目前高校理工类的公共必修课程之一,可见其重要性,C语言集高级编程语言和汇编语言的特点于一身,目前市面上的许多软件也是在C语言的平台上产生的,所以高校在C语言的教学中也可谓是＂不遗余力＂的。但是从近年来的教学效果来看,学生的普遍反映却让人大跌眼镜,大部分人认为C语言晦涩难学,学起来是非常吃力,经常是非常努力学习却达不到想要的效果,课程过后没有任何收获,甚至连一个小程序都写不出来,针对上述情况做了初步分析,并从C语言的编程技巧上提出了相关改善对策。     

Enhancing prototype reduction schemes with LVQ3-type algorithms

Various prototype reduction schemes have been reported in the literature. Foremost among these are the prototypes for nearest neighbor (PNN), the vector quantization (VQ), and the support vector machines (SVM) methods. In this paper, we shall show that these schemes can be enhanced by the introduction of a post-processing phase that is related, but not identical to, the LVQ3 process. Although the post-processing with LVQ3 has been reported for the SOM and the basic VQ methods, in this paper, we shall show that an analogous philosophy can be used in conjunction with the SVM and PNN rules. Our essential modification to LVQ3 first entails a partitioning of the respective training sets into two sets called the Placement set and the Optimizing set, which are instrumental in determining the LVQ3 parameters. Such a partitioning is novel to the literature. Our experimental results demonstrate that the proposed enhancement yields the best reported prototype condensation scheme to-date for both artificial data sets, and for samples involving real-life data sets.