A Meta-Learning Method to Select the Kernel Width in Support Vector Regression

The Support Vector Machine algorithm is sensitive to the choice of parameter settings. If these are not set correctly, the algorithm may have a substandard performance. Suggesting a good setting is thus an important problem. We propose a meta-learning methodology for this purpose and exploit information about the past performance of different settings. The methodology is applied to set the width of the Gaussian kernel. We carry out an extensive empirical evaluation, including comparisons with other methods (fixed default ranking; selection based on cross-validation and a heuristic method commonly used to set the width of the SVM kernel). We show that our methodology can select settings with low error while providing significant savings in time. Further work should be carried out to see how the methodology could be adapted to different parameter setting tasks.Supplementary material to this paper is available in electronic form at http://dx.doi.org/10.1023/B:MACH.0000015879.28004.9b  

Multilabel classification via calibrated label ranking

Label ranking studies the problem of learning a mapping from instances to rankings over a predefined set of labels. Hitherto existing approaches to label ranking implicitly operate on an underlying (utility) scale which is not calibrated in the sense that it lacks a natural zero point. We propose a suitable extension of label ranking that incorporates the calibrated scenario and substantially extends the expressive power of these approaches. In particular, our extension suggests a conceptually novel technique for extending the common learning by pairwise comparison approach to the multilabel scenario, a setting previously not being amenable to the pairwise decomposition technique. The key idea of the approach is to introduce an artificial calibration label that, in each example, separates the relevant from the irrelevant labels. We show that this technique can be viewed as a combination of pairwise preference learning and the conventional relevance classification technique, where a separate classifier is trained to predict whether a label is relevant or not. Empirical results in the area of text categorization, image classification and gene analysis underscore the merits of the calibrated model in comparison to state-of-the-art multilabel learning methods.  

代价敏感的列表排序算法

排序学习是信息检索与机器学习中的研究热点之一.在信息检索中,预测排序列表中顶部排序非常重要.但是,排序学习中一类经典的排序算法——列表排序算法——无法强调预测排序列表中顶部排序.为了解决此问题,将代价敏感学习的思想融入到列表排序算法中,提出代价敏感的列表排序算法框架.该框架是在列表排序算法的损失函数中对文档引入权重,且基于性能评价指标NDCG计算文档的权重.在此基础之上,进一步证明了代价敏感的列表排序算法的损失函数是NDCG损失的上界.为了验证代价敏感的列表排序算法的有效性,在此框架下提出了一种代价敏感的ListMLE排序算法,并对该算法开展序保持与泛化性的理论研究工作,从理论上验证了该算法具有序保持特性.在基准数据集上的实验结果表明,在预测排序列表中顶部排序中,代价敏感的ListMLE比传统排序学习算法能取得更好的性能.  

基于PRank算法的主动排序学习算法

针对排序学习中如何选择最值得标注的样本和通过尽可能少的已标注样本训练出较好的排序模型的问题，将主动学习的思想引入排序学习中，提出一种基于排序感知机的主动排序学习算法——Active PRank。基于真实数据集的实验结果表明，该算法在保证排序模型性能的前提下，减少样本的标注量，在同等标注量的条件下，提高排序结果的正确率。  

基于用户行为分析的个人信息检索研究

个人信息检索是指个人计算机上用户搜索个人信息(通常是文档)的过程,与互联网检索相比,个人信息检索能够利用的信息很少,这使得其检索结果的排序更加困难。该文通过考察计算机上的用户行为,对个人信息检索的排序问题进行深入的研究。该文考察的用户行为主要包括用户在检索系统中的查询行为和在计算机上的文件访问行为。该文一方面通过查询行为数据训练出结果排序函数,另一方面通过文件访问行为数据获取文件自身的权重,最后利用统计学习方法结合这两类行为的计算结果。实验结果表明,该文提出的方法好于传统的TFIDF排序方法。  

Ranking with decision tree

Ranking problems have recently become an important research topic in the joint field of machine learning and information retrieval. This paper presented a new splitting rule that introduces a metric, i.e., an impurity measure, to construct decision trees for ranking tasks. We provided a theoretical basis and some intuitive explanations for the splitting rule. Our approach is also meaningful to collaborative filtering in the sense of dealing with categorical data and selecting relative features. Some experiments were made to illustrate our ranking approach, whose results showed that our algorithm outperforms both perceptron-based ranking and the classification tree algorithms in term of accuracy as well as speed.

Uncooperative gait recognition by learning to rank

Gait is a useful biometric because it can operate from a distance and without subject cooperation. However, it is affected by changes in covariate conditions (carrying, clothing, view angle, etc.). Existing methods suffer from lack of training samples, can only cope with changes in a subset of conditions with limited success, and implicitly assume subject cooperation. We propose a novel approach which casts gait recognition as a bipartite ranking problem and leverages training samples from different people and even from different datasets. By exploiting learning to rank, the problem of model over-fitting caused by under-sampled training data is effectively addressed. This makes our approach suitable under a genuine uncooperative setting and robust against changes in any covariate conditions. Extensive experiments demonstrate that our approach drastically outperforms existing methods, achieving up to 14-fold increase in recognition rate under the most difficult uncooperative settings.  

Multi-task learning to rank for web search

Both the quality and quantity of training data have significant impact on the accuracy of rank functions in web search. With the global search needs, a commercial search engine is required to expand its well tailored service to small countries as well. Due to heterogeneous intrinsic of query intents and search results on different domains (i.e., for different languages and regions), it is difficult for a generic ranking function to satisfy all type of queries. Instead, each domain should use a specific well tailored ranking function. In order to train each ranking function for each domain with a scalable strategy, it is critical to leverage existing training data to enhance the ranking functions of those domains without sufficient training data. In this paper, we present a boosting framework for learning to rank in the multi-task learning context to attack this problem. In particular, we propose to learn non-parametric common structures adaptively from multiple tasks in a stage-wise way. An algorithm is developed to iteratively discover super-features that are effective for all the tasks. The estimation of the regression function for each task is then learned as linear combination of those super-features. We evaluate the accuracy of multi-task learning methods for web search ranking using data from multiple domains from a commercial search engine. Our results demonstrate that multi-task learning methods bring significant relevance improvements over existing baseline method.  

一种基于排序学习方法的查询扩展技术

查询扩展作为一门重要的信息检索技术,是以用户查询为基础,通过一定策略在原始查询中加入一些相关的扩展词,从而使得查询能够更加准确地描述用户信息需求.排序学习方法利用机器学习的知识构造排序模型对数据进行排序,是当前机器学习与信息检索交叉领域的研究热点.该文尝试利用伪相关反馈技术,在查询扩展中引入排序学习算法,从文档集合中提取与扩展词相关的特征,训练针对于扩展词的排序模型,并利用排序模型对新查询的扩展词集合进行重新排序,将排序后的扩展词根据排序得分赋予相应的权重,加入到原始查询中进行二次检索,从而提高信息检索的准确率.在TREC数据集合上的实验结果表明,引入排序学习算法有助于提高伪相关反馈的检索性能.  

基于用户行为分析的本地搜索排序算法优化

随着本地搜索的发展，通用排序算法得出的排序结果已不能完全满足用户的需要，根据本地搜索的特点，可以更好地利用用户的搜索特征。文中提出通过对用户的行为分析，提取用户行为特征值，再运用排序学习的SVM（支持向量机）方法将分析得到的用户行为特征值融入本地搜索算法当中，以此实现对排序算法的优化。融人了用户行为特征后，本地搜索的排序结果平均准确率和前十名文档的相关性都有了一定的提高。实验结果显示，用户行为特征使得排序结果可以更容易、准确地反映用户的兴趣，提升了用户的搜索体验。