417,710 research outputs found
Solving Multiclass Learning Problems via Error-Correcting Output Codes
Multiclass learning problems involve finding a definition for an unknown
function f(x) whose range is a discrete set containing k > 2 values (i.e., k
``classes''). The definition is acquired by studying collections of training
examples of the form [x_i, f (x_i)]. Existing approaches to multiclass learning
problems include direct application of multiclass algorithms such as the
decision-tree algorithms C4.5 and CART, application of binary concept learning
algorithms to learn individual binary functions for each of the k classes, and
application of binary concept learning algorithms with distributed output
representations. This paper compares these three approaches to a new technique
in which error-correcting codes are employed as a distributed output
representation. We show that these output representations improve the
generalization performance of both C4.5 and backpropagation on a wide range of
multiclass learning tasks. We also demonstrate that this approach is robust
with respect to changes in the size of the training sample, the assignment of
distributed representations to particular classes, and the application of
overfitting avoidance techniques such as decision-tree pruning. Finally, we
show that---like the other methods---the error-correcting code technique can
provide reliable class probability estimates. Taken together, these results
demonstrate that error-correcting output codes provide a general-purpose method
for improving the performance of inductive learning programs on multiclass
problems.Comment: See http://www.jair.org/ for any accompanying file
Weblogs in Higher Education - Why Do Students (Not) Blog?
Positive impacts on learning through blogging, such as active knowledge construction and reflective writing, have been reported. However, not many students use weblogs in informal contexts, even when appropriate facilities are offered by their universities. While motivations for blogging have been subject to empirical studies, little research has addressed the issue of why students choose not to blog. This paper presents an empirical study undertaken to gain insights into the decision making process of students when deciding whether to keep a blog or not. A better understanding of students' motivations for (not) blogging may help decision makers at universities in the process of selecting, introducing, and maintaining similar services. As informal learning gains increased recognition, results of this study can help to advance appropriate designs of informal learning contexts in Higher Education. The method of ethnographic decision tree modelling was applied in an empirical study conducted at the Vienna University of Technology, Austria. Since 2004, the university has been offering free weblog accounts for all students and staff members upon entering school, not bound to any course or exam. Qualitative, open interviews were held with 3 active bloggers, 3 former bloggers, and 3 non‑ bloggers to elicit their decision criteria. Decision tree models were developed out of the interviews. It turned out that the modelling worked best when splitting the decision process into two parts: one model representing decisions on whether to start a weblog at all, and a second model representing criteria on whether to continue with a weblog once it was set up. The models were tested for their validity through questionnaires developed out of the decision tree models. 30 questionnaires have been distributed to bloggers, former bloggers and non‑ bloggers. Results show that the main reasons for students not to keep a weblog include a preference for direct (online) communication, and concerns about the loss of privacy through blogging. Furthermore, the results indicate that intrinsic motivation factors keep students blogging, whereas stopping a weblog is mostly attributable to external factors
Secure Two-Party Protocol for Privacy-Preserving Classification via Differential Privacy
Privacy-preserving distributed data mining is the study of mining on distributed data—owned by multiple data owners—in a non-secure environment, where the mining protocol does not reveal any sensitive information to the data owners, the individual privacy is preserved, and the output mining model is practically useful. In this thesis, we propose a secure two-party protocol for building a privacy-preserving decision tree classifier over distributed data using differential privacy. We utilize secure multiparty computation to ensure that the protocol is privacy-preserving. Our algorithm also utilizes parallel and sequential compositions, and applies distributed exponential mechanism to ensure that the output is differentially-private. We implemented our protocol in a distributed environment on real-life data, and the experimental results show that the protocol produces decision tree classifiers with high utility while being reasonably efficient and scalable
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