Yahoo!

About: Yahoo! is a company organization based out in London, United Kingdom. It is known for research contribution in the topics: Population & Web search query. The organization has 26749 authors who have published 29915 publications receiving 732583 citations. The organization is also known as: Yahoo! Inc. & Maudwen-Yahoo! Inc.

Method and system for seeding online social network contacts

Abstract: A method, apparatus, and system are directed towards seeding a user's contacts for their online social network. The invention is arranged to automatically recommend to the user a set of seed contacts that the user may employ to invite to join their social network. The set of seed contacts may be harvested from the user's existing portal activities, as well as other sources. In one embodiment, the invention analyzes portal activity, such as email exchanges with the user, and the like, to determine a frequency of contact with the user. Other sources may include but not be limited to emails, names within an address book of the user, names within an address book of another person, a buddy list, an instant messaging list, an activity, a mailing list, an online discussion group, a membership in a category, chat group, and the like.

Leveraging social media networks for classification

Abstract: Social media has reshaped the way in which people interact with each other. The rapid development of participatory web and social networking sites like YouTube, Twitter, and Facebook, also brings about many data mining opportunities and novel challenges. In particular, we focus on classification tasks with user interaction information in a social network. Networks in social media are heterogeneous, consisting of various relations. Since the relation-type information may not be available in social media, most existing approaches treat these inhomogeneous connections homogeneously, leading to an unsatisfactory classification performance. In order to handle the network heterogeneity, we propose the concept of social dimension to represent actors' latent affiliations, and develop a classification framework based on that. The proposed framework, SocioDim, first extracts social dimensions based on the network structure to accurately capture prominent interaction patterns between actors, then learns a discriminative classifier to select relevant social dimensions. SocioDim, by differentiating different types of network connections, outperforms existing representative methods of classification in social media, and offers a simple yet effective approach to integrating two types of seemingly orthogonal information: the network of actors and their attributes.

Feature selection via dependence maximization

Abstract: We introduce a framework for feature selection based on dependence maximization between the selected features and the labels of an estimation problem, using the Hilbert-Schmidt Independence Criterion. The key idea is that good features should be highly dependent on the labels. Our approach leads to a greedy procedure for feature selection. We show that a number of existing feature selectors are special cases of this framework. Experiments on both artificial and real-world data show that our feature selector works well in practice.

Target-based agents against ErbB receptors and their ligands: a novel approach to cancer treatment.

Abstract: The ErbB receptors and their cognate ligands that belong to the epidermal growth factor (EGF) family of peptides are involved in the pathogenesis of different types of carcinomas. In fact, the ErbB receptors and the EGF-like growth factors are frequently expressed in human tumors. These proteins form a complex system that regulates the proliferation and the survival of cancer cells. Therefore, ErbB receptors and their ligands might represent suitable targets for novel therapeutic approaches in human carcinomas. In this regard, different target-based agents that are directed against the ErbB receptors have been developed in the past two decades. One of these compounds, the humanized anti-ErbB-2 monoclonal antibody trastuzumab has been approved for the treatment of patients with metastatic breast cancer. The anti-EGF receptor (EGFR) antibody C225, as well as EGFR tyrosine kinase inhibitors ZD1839 and OSI-774 are currently in phase III clinical development. Several other ErbB tyrosine kinase inhibitors are in phase I/II studies. These compounds have generally been shown to have an acceptable toxicity profile and promising anti-tumor activity in heavily pretreated patients. The mechanisms of action of these compounds, as well as the potential therapeutic strategies to improve their efficacy are discussed in this review with particular regard to the combinations of anti-ErbB agents with cytotoxic drugs, or combinations of different ErbB-targeting agents.

Slow Learners are Fast

Abstract: Online learning algorithms have impressive convergence properties when it comes to risk minimization and convex games on very large problems. However, they are inherently sequential in their design which prevents them from taking advantage of modern multi-core architectures. In this paper we prove that online learning with delayed updates converges well, thereby facilitating parallel online learning.