Transfer Learning for Cross-Dataset Recognition: A Survey <s> Spam Email Filtering <s> Automated classification of email messages into user-specific folders and information extraction from chronologically ordered email streams have become interesting areas in text learning research. However, the lack of large benchmark collections has been an obstacle for studying the problems and evaluating the solutions. In this paper, we introduce the Enron corpus as a new test bed. We analyze its suitability with respect to email folder prediction, and provide the baseline results of a state-of-the-art classifier (Support Vector Machines) under various conditions, including the cases of using individual sections (From, To, Subject and body) alone as the input to the classifier, and using all the sections in combination with regression weights. <s> BIB001 </s> Transfer Learning for Cross-Dataset Recognition: A Survey <s> Spam Email Filtering <s> We study a setting that is motivated by the problem of filtering spam messages for many users. Each user receives messages according to an individual, unknown distribution, reflected only in the unlabeled inbox. The spam filter for a user is required to perform well with respect to this distribution. Labeled messages from publicly available sources can be utilized, but they are governed by a distinct distribution, not adequately representing most inboxes. We devise a method that minimizes a loss function with respect to a user's personal distribution based on the available biased sample. A nonparametric hierarchical Bayesian model furthermore generalizes across users by learning a common prior which is imposed on new email accounts. Empirically, we observe that bias-corrected learning outperforms naive reliance on the assumption of independent and identically distributed data; Dirichlet-enhanced generalization across users outperforms a single ("one size fits all") filter as well as independent filters for all users. <s> BIB002 </s> Transfer Learning for Cross-Dataset Recognition: A Survey <s> Spam Email Filtering <s> We introduce a challenging set of 256 object categories containing a total of 30607 images. The original Caltech-101 [1] was collected by choosing a set of object categories, downloading examples from Google Images and then manually screening out all images that did not fit the category. Caltech-256 is collected in a similar manner with several improvements: a) the number of categories is more than doubled, b) the minimum number of images in any category is increased from 31 to 80, c) artifacts due to image rotation are avoided and d) a new and larger clutter category is introduced for testing background rejection. We suggest several testing paradigms to measure classification performance, then benchmark the dataset using two simple metrics as well as a state-of-the-art spatial pyramid matching [2] algorithm. Finally we use the clutter category to train an interest detector which rejects uninformative background regions. <s> BIB003 </s> Transfer Learning for Cross-Dataset Recognition: A Survey <s> Spam Email Filtering <s> In real-world applications of visual recognition, many factors — such as pose, illumination, or image quality — can cause a significant mismatch between the source domain on which classifiers are trained and the target domain to which those classifiers are applied. As such, the classifiers often perform poorly on the target domain. Domain adaptation techniques aim to correct the mismatch. Existing approaches have concentrated on learning feature representations that are invariant across domains, and they often do not directly exploit low-dimensional structures that are intrinsic to many vision datasets. In this paper, we propose a new kernel-based method that takes advantage of such structures. Our geodesic flow kernel models domain shift by integrating an infinite number of subspaces that characterize changes in geometric and statistical properties from the source to the target domain. Our approach is computationally advantageous, automatically inferring important algorithmic parameters without requiring extensive cross-validation or labeled data from either domain. We also introduce a metric that reliably measures the adaptability between a pair of source and target domains. For a given target domain and several source domains, the metric can be used to automatically select the optimal source domain to adapt and avoid less desirable ones. Empirical studies on standard datasets demonstrate the advantages of our approach over competing methods. <s> BIB004
The ECML/PKDD 2006 Challenge focuses on the personalized spam filtering tasks. It aims at building a spam filter for automatically detecting spam emails. Training such filters rely on publicly available sources. However, the spam emails are person specific, suggesting that the distributions of the combined source of training data are different from that of emails received by individual users. In this challenge, a spam email filtering dataset was provided 6 . Another cross domain spam email dataset was created by BIB002 , which is built upon Enron Corpus 7 BIB001 . The dataset contains nine different inboxes with test emails (5270 to 10964 emails, depending on inbox) and one set of training emails collected from various different sources.  create the 20 Newsgroups data set 8 for the task of cross domain text classification algorithms, which contains 20,000 newsgroup documents, partitioned (nearly) evenly across 20 different newsgroups corresponding to different topics. The goal is to distinguish documents from newsgroup categories, such as between rec and talk.  create the Office dataset 9 for evaluating the effects of domain shift. Three datasets representing three domains are involved in the Office dataset, which are: Amazon (images downloaded from online merchants), Webcam (low-resolution images by a web camera), DSLR (high-resolution images by a digital SLR camera). Thirty one object categories are contained in the three datasets. BIB004 combine the Caltech-256 BIB003 ] dataset with the Office dataset  , constituting Office+Caltech object dataset 10 . The Caltech-256 dataset contains 256 object classes downloaded from Google images. Ten classes shared by four datasets are generally selected for the cross dataset object recognition task.