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Matches in ScholarlyData for { ?s ?p ?o. }

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• item-3 hasContent jian-pei.
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• 21 type InProceedings.
• 21 label "A Class-Feature-Centroid Classifier for Text Categorization".
• 21 sameAs 21.
• 21 abstract "Automated text categorization is an important technique for many web applications, such as document indexing, document filtering, and cataloging web resources. Many different approaches have been proposed for the automated text categorization problem. Among them, centroid-based approaches have the advantages of short training time and testing time due to its computational efficiency. As a result, centroid-based classifiers have been widely used in many web applications. However, the accuracy of centroid-based classifiers is inferior to SVM, mainly because centroids found during construction are far from perfect locations. We design a fast Class-Feature-Centroid (CFC) classifier for multi-class, single-label text categorization. In CFC, a centroid is built from two important class distributions: inter-class term index and inner-class term index. CFC proposes a novel combination of these indices and employs a denormalized cosine measure to calculate the similarity score between a text vector and a centroid. Experiments on the Reuters-21578 corpus and 20-newsgroup email collection show that CFC consistently outperforms the state-of-the-art SVM classifiers on both micro-F1 and macro-F1 scores. Particularly, CFC is more effective and robust than SVM when data is sparse.".
• 21 hasAuthorList authorList.
• 21 isPartOf proceedings.
• 21 keyword "Data Mining".
• 21 title "A Class-Feature-Centroid Classifier for Text Categorization".
• authorList type List.
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• item-1 hasContent hu-guan.
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• item-2 hasContent jingyu-zhou.
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• item-3 type ListItem.
• item-3 hasContent minyi-guo.
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• 22 type InProceedings.
• 22 label "Large Scale Multi-Label Classification via MetaLabeler".
• 22 sameAs 22.
• 22 abstract "The explosion of online content has made the management of such content non-trivial. Web-related tasks such as web page categorization, news filtering, query categorization, tag recommendation, etc. often involve the construction of multilabel categorization systems on a large scale. Existing multilabel classification methods either do not scale or have unsatisfactory performance. In this work, we propose MetaLabeler to automatically determine the relevant set of labels for each instance without intensive human involvement or expensive cross-validation. Extensive experiments conducted on benchmark data show that the MetaLabeler tends to outperform existing methods. Moreover, MetaLabeler scales to millions of multi-labeled instances and can be deployed easily. This enables us to apply the MetaLabeler to a large scale query categorization problem in Yahoo!, yielding a significant improvement in performance.".
• 22 hasAuthorList authorList.
• 22 isPartOf proceedings.
• 22 keyword "Data Mining".
• 22 title "Large Scale Multi-Label Classification via MetaLabeler".
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• item-1 type ListItem.
• item-1 hasContent lei-tang.
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• item-2 type ListItem.
• item-2 hasContent suju-rajan.
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• item-3 type ListItem.
• item-3 hasContent vijay-k-narayanan.
• item-3 previous item-2.
• 23 type InProceedings.
• 23 label "Hybrid Keyword Search Auctions".
• 23 sameAs 23.
• 23 abstract "Search auctions have become a dominant source of revenue generation on the Internet. Such auctions have typically used per-click bidding and pricing. We propose the use of hybrid auctions where an advertiser can make a per-impression as well as a per-click bid, and the auctioneer then chooses one of the two as the pricing mechanism. We assume that the advertiser and the auctioneer both have separate beliefs (called priors) on the click-probability of an advertisement. We first prove that the hybrid auction is truthful, assuming that the advertisers are risk-neutral. We then show that this auction is superior to the existing per-click auction in multiple ways: 1. We show that risk-seeking advertisers will choose only a per-impression bid whereas risk-averse advertisers will choose only a per-click bid, and argue that both kind of advertisers arise naturally. Hence, the ability to bid in a hybrid fashion is important to account for the risk characteristics of the advertisers. 2. For obscure keywords, the auctioneer is unlikely to have a very sharp prior on the click-probabilities. In such situations, we show that having the extra information from the advertisers in the form of a perimpression bid can result in significantly higher revenue. 3. An advertiser who believes that its click-probability is much higher than the auctioneer's estimate can use per-impression bids to correct the auctioneer's prior without incurring any extra cost. 4. The hybrid auction can allow the advertiser and auctioneer to implement complex dynamic programming strategies to deal with the uncertainty in the clickprobability using the same basic auction. The per-click and per-impression bidding schemes can only be used to implement two extreme cases of these strategies. ∗Research supported in part by NSF ITR grant 0428868, by gifts from Google, Microsoft, and Cisco, and by the Stanford-KAUST alliance. †Research supported by NSF via a CAREER award and grant CNS-0540347.".
• 23 hasAuthorList authorList.
• 23 isPartOf proceedings.
• 23 keyword "Internet Monetization".
• 23 title "Hybrid Keyword Search Auctions".
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• authorList hasFirstItem item-1.
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• item-1 type ListItem.
• item-1 hasContent ashish-goel.
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• item-2 hasContent kamesh-munagala.
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• 24 type InProceedings.
• 24 label "Bid Optimization for Broad Match Ad Auctions".
• 24 sameAs 24.
• 24 abstract "Ad auctions in sponsored search support "broad match" that allows an advertiser to target a large number of queries while bidding only on a limited number. While giving more expressiveness to advertisers, this feature makes it challenging to optimize bids to maximize their returns: choosing to bid on a query as a broad match because it provides high profit results in one bidding for related queries which may yield low or even negative profits. We abstract and study the complexity of the bid optimization problem which is to determine an advertiser's bids on a subset of keywords (possibly using broad match) so that her profit is maximized. In the query language model when the advertiser is allowed to bid on all queries as broad match, we present a linear programming (LP)-based polynomialtime algorithm that gets the optimal profit. In the model in which an advertiser can only bid on keywords, ie., a subset of keywords as an exact or broad match, we show that this problem is not approximable within any reasonable approximation factor unless P=NP. To deal with this hardness result, we present a constant-factor approximation when the optimal profit significantly exceeds the cost. This algorithm is based on rounding a natural LP formulation of the problem. Finally, we study a budgeted variant of the problem, and show that in the query language model, one can find two budget constrained ad campaigns in polynomial time that implement the optimal bidding strategy. Our results are the first to address bid optimization under the broad match feature which is common in ad auctions.".
• 24 hasAuthorList authorList.
• 24 isPartOf proceedings.
• 24 keyword "Internet Monetization".
• 24 title "Bid Optimization for Broad Match Ad Auctions".
• authorList type List.
• authorList hasFirstItem item-1.
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• authorList hasItem item-5.
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• item-1 type ListItem.
• item-1 hasContent eyal-even-dar.
• item-1 next item-2.
• item-2 type ListItem.
• item-2 hasContent vahab-s-mirrokni.

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