Tag/
共41个网摘 [
1 2 ]
下一页 |
tonywjd收录，使用标签：Opensource, ai, weka，时间：2008-4-13 13:03:49 | 相关网摘，我也收藏
Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.
Weka is open source software issued under the GNU General Public License.
http://www.cs.waikato.ac.nz/ml/weka/index_home.html
zdg收录，使用标签：新闻, Search, AI，时间：2008-3-29 23:47:18 | 相关网摘，我也收藏
粗略总结下的话，搜索新闻的缺陷大概体现在以下几个方面：
1，新闻源的过渡分散、部分选取新闻源质量低下。
相比人工编辑模式，搜索能聚合成千上万的新闻源信息。这给新闻聚合也带来了麻烦，过于分散的新闻源显示，让读者缺乏对内容的认可和信任感，甚至体现在源页面上。techmeme的例子告诉我们，选取更重要权威的新闻源(或提高权重度)能增加读者的信赖度，同时避免低质量内容出现。
2，新闻信息的分裂，未按读者阅读需求建立关系。
搜索通过语义分析解决了新闻信息的相关性，但在信息关系的梳理上，变得束手无策。适当的人工干预，说不定能达到不错的效果。比如，此前我一再强调的“新闻 观点”模式，其中，观点和新闻本身又被不断打散在杂志、报纸以及blogosphere等中。
3，新闻的连续性缺失。
我在前不久的“新闻信息的种类、关系和组织”中，已经谈过这一点。搜索聚合新闻，一个很大的弊端就是没有反应新闻信息本身的连续性关系和回放。搜索很方便的提供了读者到达某一条新闻的通道，但却没能力把它们按新闻事件本身组织好。
4，缺乏必要的新闻回放及归档。
5，无限接近的相关性。
注：以上所指搜索新闻，主要是基于搜索平台聚合新闻。
http://www.caozenghui.cn/?p=281
tonywjd收录，使用标签：ai, svm，时间：2008-1-25 16:42:21 | 相关网摘，我也收藏
To solve this optimization problem, SVMmulticlass uses an algorithm that is different from the one in [1]. The algorithm is based on Structural SVMs [2] and it is an instance of SVMstruct. For linear kernels, SVMmulticlass V2.12 is very fast and runtime scales linearly with the number of training examples. Non-linear kernels are not (really) supported. It also serves as a easy tutorial example of how to use the SVMstruct programming interface. More information on SVMstruct is available here.
http://www.cs.cornell.edu/People/tj/svm%5Flight/svm_multiclass.html
tonywjd收录，使用标签：ai, svm，时间：2008-1-24 10:29:20 | 相关网摘，我也收藏
SVMlight is an implementation of Support Vector Machines (SVMs) in C. The main features of the program are the following:
fast optimization algorithm
working set selection based on steepest feasible descent
"shrinking" heuristic
caching of kernel evaluations
use of folding in the linear case
solves classification and regression problems. For multivariate and structured outputs use SVMstruct.
solves ranking problems (e. g. learning retrieval functions in STRIVER search engine).
computes XiAlpha-estimates of the error rate, the precision, and the recall
efficiently computes Leave-One-Out estimates of the error rate, the precision, and the recall
includes algorithm for approximately training large transductive SVMs (TSVMs) (see also Spectral Graph Transducer)
can train SVMs with cost models and example dependent costs
allows restarts from specified vector of dual variables
handles many thousands of support vectors
handles several hundred-thousands of training examples
supports standard kernel functions and lets you define your own
uses sparse vector representation
http://www.cs.cornell.edu/People/tj/svm%5Flight/
kaigelee收录，使用标签：ai，时间：2006-11-18 11:56:35 | 相关网摘，我也收藏
"AI can have two purposes. One is to use the power of computers to augment human thinking, just as we use motors to augment human or horse power. Robotics and expert systems are major branches of that. The other is to use a computer's artificial intelligence to understand how humans think. In a humanoid way. If you test your programs not merely by what they can accomplish, but how they accomplish it, they you're really doing cognitive science; you're using AI to understand the human mind."
http://www.internationalai.org/AITopics/html/cogsci.html
zdg收录，使用标签：围棋, AI，时间：2006-12-19 21:10:41 | 相关网摘，我也收藏
电脑围棋的挑战性在于要扩展当前的围棋理论或发展新理论——特别是与评估有关的，针对实时限制设计合适的数据结构和算法，解决知识瓶颈。目前还没有一个有 力的程序使用学习技术，尽管有过几次这样的尝试（如，Pell，1991；Schraudolph, Dayan & Sejnowski，1994；Donnelly, Corr & Crookes，1994）。回顾这些程序已经超出了本文的范围，但我们推测这些程序也没有成功，因为它们的设计者的含蓄的围棋理论缺乏对围棋复杂性的必 要理解。怎样把学习能力赋予现有的程序（或者它们暗示的围棋理论）是个等待解决的问题。
http://news.csdn.net/n/20061219/99613.html
共41个网摘 [
1 2 ]
下一页