As the Internet-based crowdsourcing services become more and more popular, learning from multiple teachers or sources has received more attention of the researchers in the machine learning area. In this setting, the learning system is dealing with samples and labels provided by multiple teachers, who in common cases, are non-expert. Their labeling styles and behaviors are usually diverse, some of which are even detrimental to the learning system. Thus, simply putting them together and utilizing the algorithms designed for single-teacher scenario would be not only improper, but also damaging. The problem calls for more specific methods. Our work focuses on a case where the teachers are composed of good ones and irresponsible ones. By irresponsible, we mean the teacher who takes the labeling task not seriously and label the sample at random without inspecting the sample itself. This behavior is quite common when the task is not attractive enough and the teacher just wants to finish it as soon as possible. Sometimes, the irresponsible teachers could take a considerable part among all the teachers. If we do not take out their effects, our learning system would be ruined with no doubt. In this paper, we propose a method for picking out the good teachers with promising experimental results. It works even when the irresponsible teachers are dominating in numbers.

In this paper, we point out that there exist scaling and initialization problems in most existing multiple kernel learning (MKL) approaches, which employ the large margin principle to jointly learn both a kernel and an SVM classifier. The reason is that the margin itself can not well describe how good a kernel is due to the negligence of the scaling. We use the ratio between the margin and the radius of the minimum enclosing ball to measure the goodness of a kernel, and present a new minimization formulation for kernel learning. This formulation is invariant to scalings of learned kernels, and when learning linear combination of basis kernels it is also invariant to scalings of basis kernels and to the types (e.g., L1 or L2) of norm constraints on combination coefficients. We establish the differentiability of our formulation, and propose a gradient projection algorithm for kernel learning. Experiments show that our method significantly outperforms both SVM with the uniform combination of basis kernels and other state-of-art MKL approaches.

In this paper, we consider a recently proposed supervised learning problem, called online multiclass prediction with bandit setting model. Aiming at learning from partial feedback of online classification results, i.e. ¿true¿ when the predicting label is right or ¿false¿ when the predicting label is wrong, this new kind of problems arouses much of researchers' interest due to its close relations to real world internet applications and human cognitive procedure. While some algorithms have been brought forward, we propose a novel algorithm to deal with such problems. First, we reduce the multiclass prediction problem to binary based on Conservative one-versus-all others Reduction scheme; Then Online Passive-Aggressive Algorithm is embedded as binary learning algorithm to solve the reduced problem. Also we derive a pleasing cumulative mistake bound for our algorithm and a time complexity bound linear to the sample size. Further experimental evaluation on several real world multiclass datasets including RCV1, MNIST, 20 Newsgroups and USPS shows that our method outperforms the existing algorithms with a great improvement.