We present a novel method for multitask learning (MTL) based on {\it manifold regularization}: assume that all task parameters lie on a manifold. This is the generalization of a common assumption made in the existing literature: task parameters share a common {\it linear} subspace. One proposed method uses the projection distance from the manifold to regularize the task parameters. The manifold structure and the task parameters are learned using an alternating optimization framework. When the manifold structure is fixed, our method decomposes across tasks which can be learnt independently. An approximation of the manifold regularization scheme is presented that preserves the convexity of the single task learning problem, and makes the proposed MTL framework efficient and easy to implement. We show the efficacy of our method on several datasets.

Determining whether someone is talking has applications in many areas such as speech recognition, speaker diarization, social robotics, facial expression recognition, andhuman computer interaction. One popular approach to this problem is audiovisual synchrony detection [10, 21, 12]. A candidate speaker is deemed to be talking if the visual signal around that speaker correlates with the auditory signal. Here we show that with the proper visual features (in this case movements of various facial muscle groups), a very accurate detector of speech can be created thatdoes not use the audio signal at all. Further we show that this person independent visual-only detector can be used to train very accurate audio-based person dependent voice models. The voice model has the advantage of being able to identify when a particular person is speaking even when they are not visible to the camera (e.g. in the case of a mobile robot). Moreover, we show that a simple sensory fusion scheme between the auditory and visual models improves performance onthe task of talking detection. The work here provides dramatic evidence about the efficacy of two very different approaches to multimodal speech detection on a challenging database.

A learning algorithm based on primary school teaching and learning is presented. The methodology is to continuously evaluate a student and to give them training on the examples for which they repeatedly fail, until, they can correctly answer all types of questions. This incremental learning procedure produces better learning curves by demanding the student to optimally dedicate their learning time on the failed examples. When used in machine learning, the algorithm is found to train a machine on a data with maximum variance in the feature space so that the generalization ability of the network improves. The algorithm has interesting applications in data mining, model evaluations and rare objects discovery.

We present a probabilistic logic programming framework to reinforcement learning, by integrating reinforce-ment learning, in POMDP environments, with normal hybrid probabilistic logic programs with probabilistic answer set seman-tics, that is capable of representing domain-specific knowledge. We formally prove the correctness of our approach. We show that the complexity of finding a policy for a reinforcement learning problem in our approach is NP-complete. In addition, we show that any reinforcement learning problem can be encoded as a classical logic program with answer set semantics. We also show that a reinforcement learning problem can be encoded as a SAT problem. We present a new high level action description language that allows the factored representation of POMDP. Moreover, we modify the original model of POMDP so that it be able to distinguish between knowledge producing actions and actions that change the environment.

The problem of maximum-likelihood (ML) estimation of discrete tree-structured distributions is considered. Chow and Liu established that ML-estimation reduces to the construction of a maximum-weight spanning tree using the empirical mutual information quantities as the edge weights. Using the theory of large-deviations, we analyze the exponent associated with the error probability of the event that the ML-estimate of the Markov tree structure differs from the true tree structure, given a set of independently drawn samples. By exploiting the fact that the output of ML-estimation is a tree, we establish that the error exponent is equal to the exponential rate of decay of a single dominant crossover event. We prove that in this dominant crossover event, a non-neighbor node pair replaces a true edge of the distribution that is along the path of edges in the true tree graph connecting the nodes in the non-neighbor pair. Using ideas from Euclidean information theory, we then analyze the scenario of ML-estimation in the very noisy learning regime and show that the error exponent can be approximated as a ratio, which is interpreted as the signal-to-noise ratio (SNR) for learning tree distributions. We show via numerical experiments that in this regime, our SNR approximation is accurate.

Fast mapping is a phenomenon by which children learn the meanings of novel adjectives after a very small number of exposures when the new word is contrasted with a known word. The present study was a preliminary test of whether machine learners could use such contrasts in unconstrained speech to learn adjective meanings and categories. Six decision tree-based learning methods were evaluated that use contrasting examples in order to work toward an adjective fast-mapping system for machine learners. Subjects tended to compare objects using adjectives of the same category, implying that such contrasts may be a useful source of data about adjective meaning, though none of the learning algorithms showed strong advantages over any other.

Inconsistency in behaviors of virtual agents and robots, like that between utterance contents, utterance forms, and postures, has a possibility of influences into human impression, cognition, and memory, and as a result, may lead to inhibition of dialogues between humans and these artifacts. In order to discuss about this possibility and its implications on dialogue design, this paper introduces some case studies using simple animated characters and a small-sized humanoid robot in Japan.

But moreover, the implicit nature Metacognitive activity is a core aspect of many multifaceted of metacognitive activities makes the goal of supporting practices, but supporting such activity in educational contexts metacognition perhaps an even larger challenge. When we is a complex endeavor. One example of such a practice think about the two major learning goals described above includes the substantive inquiry practices that different in the science inquiry example and other learning goals educational policy groups (for example, National Research put forth in many educational policies, we can the central Council 2000) recommend for K-12 student curricula, including challenge that we want to address with metacognitive support: those practices that involve more authentic types of (1) supporting novice learners to mindfully engage in scientific inquiry along with online inquiry activities that incorporate the metacognitive activity necessary to successfully participate a growing number of digital libraries and other in complex, multifaceted practices, and (2) supporting information resources. There are many characterizations novice learners to learn good metacognitive practiceswhat of inquiry, but we can succinctly describe inquiry as a set metacognitive activities are, why they are important, and of activities that involve: (1) asking and developing questions how to engage in them. Supporting metacognition is vital to investigate; (2) searching for and gathering relevant to essentially help make these implicit activities more explicit data and information; (3) reading, evaluating, and analyzing to learners, yet we continue to see how difficult it is to the gathered data and information; and (4) synthesizing provide such support.

Meta-cognitive skills to be developed in learning for the 21st century is the detection and correction of errors in solutions. These meta-cognitive skills can help to detect errors the learner has made her/himself as well as errors others have made. Our investigations in learning from errors have the ultimate goal to adapt the selection and presentation to the learner so that he/she can better learn from erroneous examples others have made. In our experiments we found that (1) erroneous examples with help provision can promote students skill of find errors, (2) the benefit from erroneous examples depends on the relation between the student's level and the example's difficulty, i.e. if the student is prepared for the problem, (3) for many students it is very difficult to correct errors.

A well-documented finding in the help-seeking literature is that especially those learners who need it the most do not seek help (appropriately). In this exploratory study, we investigated re-reading as a unique window into elementary help-seeking processes. Students had to learn the content of multiple hypertext pages of different complexity for a subsequent knowledge test. After this learning phase we randomly assigned learners to two experimental groups: The memory control group (MG, n = 14) directly answered the knowledge test and the experimental help-seeking group (HSG, n = 15) had the option to re-read the hypertext pages before answering. Results show that HSG students outperformed MG students and that HSG students strongly adapted the extent and frequency of their re-reading to task complexity and the complexity of the hypertext pages. However, more re-reading or more adaptivity did not automatically enhance performance on the knowledge test. The implications of these findings for the design of adaptive (metacognitive) help functions in computer-based learning environments will be discussed.