Semi-supervised learning belongs to the main directions of the recent machine learning research. The exploitation of the unlabeled data is an attractive approach either to extend the capability of the known methods or to derive novel learning devices.  Learning a rule from a finite sample is the fundamental problem of machine learning. For this purpose two resources are needed: a big enough sample and enough computational power. While the computational power has been growing rapidly, the cost of collecting a large sample remains high since it is labour intensive. The unlabeled data can be used to find a compact representation of the data which preserves as much as possible its original structure.

This PASCAL funded pump-priming project aims to bring continuous and uncertain interaction methods into both brain-computer interfaces, and to the interactive exploration of song spaces. By treating the interaction problem as a continuous control process, a range of novel techniques can be brought to bear on the BCI and song exploration problem. EEG brain-computer interfaces suffer from high noise levels and heavily-lagged dynamics. Existing user interface models are inefficient and frustrating for interaction. By explictly taking the noise and dynamical properties of the BCI control signals into account, more suitable interfaces can be devised. The song-exploration problem involves navigation of very high-dimesional feature spaces. The mapping from these spaces to user intention is uncertain. Uncertain and predictive displays, combined with intelligent navigation controls, can aid users in intuitively navigating musical spaces. This work is in collaboration with the IDA group at Fraunhofer First and the Intelligent Signal Processsing Group at DTU.

Research on large margin algorithms in conjunctions with kernel methods has been both exciting and successful. While there have been quite a few preliminary successes in applying kernel methods for speech applications, most research efforts have focused on non-temporal problems such as text classification and optical character recognition (OCR). We propose to design, analyze, and implement learning algorithms and kernels for hierarchical-temporal speech utterances. Our first and primary end-goal is to build and test thoroughly a full-blown speech phoneme classifier that will be trained on millions of examples and will achieve the best results in this domain. This project is a joint reseach effort between The Hebrew University and IDIAP.

This project is focused on multi-task learning (MTL) for the purposes of developing optimisation methods, statistical analysis and applications. On the theoretical side, we propose to develop a new generation of MTL algorithms; on the practical side, we will explore applications of these algorithms in the areas of marketing science, bioinformatics and robot learning. As an increasing number of data analysis problems require learning from multiple data sources, MTL should receive more attention in Machine Learning and we expect that more researchers will work on this topic in the coming years. We are particularly interested in optimisation approaches to MTL. In particular, our proposed approach will: 1) allow one to model constraints among the tasks; 2) allow semi-supervised learning — only some of the tasks have available data but we still wish to learn all tasks; 3) lead to efficient optimisation algorithms; 4) subsume related frameworks such as collaborative filtering and learning vector fields.

The goal of this project is to investigate whether image representations based on local invariant features, and document analysis algorithms such as probabilistic latent semantic analysis, can be successfully adapted and combined for the specific problem of scene categorisation. More precisely, our aim is to distinguish between indoor/outdoor or city/landscape images, as well as (in a later stage) more diverse scene categories. This is interesting in its own right in the context of image retrieval or automatic image annotation, and also helps to provide context information to guide other processes such as object recognition or categorisation. So far, the intuitive analogy between local invariant features in an image and words in a text document has only been explored at the level of object rather than scene categories. Moreover, it has mostly been limited to a bags-of-keywords representation. Introducing visual equivalents for more evolved text retrieval methods to deal with word stemming, spatial relations between words, synonyms and polysemy is the prime research objective of this project, as well as studying the statistics of the extracted local features to determine to which degree the analogy between local visual features and words really holds in the context of scene classification, or how the local features based description needs to be adapted to make it hold.

Semantic information recognition and extraction is the major enabler for next generation information retrieval and natural language processing. Yet it is currently only successful in small domains of limited scope. We claim that to move beyond this restriction requires one: (1) to perform integrated semantic extraction incorporating a probabilistic representation of semantic content, and (2) to better employ the broader semantic resources now coming on-line. This project will explore both fundamental research and large scale applications, using the public domain Wikipedia as a driver and a resource. Research will explore the integration of semantic information into the language processing chain. Applications will employ this in broad spectrum named-entity recognition, and in cross-lingual information retrieval using the rich but incomplete data available fron the Wikipedia. Three PASCAL sites will contribute pre-existing software, theory, and skills to the range of tasks involved.

The purpose of the project is to explore a new family of grammatical inference algorithms, based on the use of string kernels. These algorithms are capable of efficiently learning some languages that are context sensitive, including many linguistically interesting examples of mildly context sensitive languages. The project started on November 1st 2005, and finished at the end of October 2006. It is a collaboration between Royal Holloway and EURISE.

This project develops new kinds of information retrieval systems, by fusing multimodal implicit relevance feedback data with text content using Bayesian and kernel-based machine learning methods. A long term goal of information retrieval is to understand the “user’s intent”. We will study the feasibility of directly measuring the interests at the sentence level, and of coupling the results to other relevant sources to estimate user preferences. The concrete task is to predict relevance for new documents given judgments on old ones. Such predictions can be used in information retrieval, and the most relevant documents can even be proactively offered to the user. The motivation for this project is that by using eye movements we wish to get rid of part of the tedious ranking of retrieved documents, called relevance feedback in standard information retrieval. Moreover, by using the potentially richer relevance feedback signal we want to access more subtle cues of relevance in addition to the usual binary relevance judgments. The major task in this research is to improve the predictions by combining eye movements with the text content. We aim at combining the relevance feedback to textual content to infer relevant words, concepts, and sentences. We combine two data sources for predicting relevance: eye movements measured during reading and the text content. This is challenging: time series models of very noisy data need to be combined with text models in a task where we typically only have very little data about relevance available. This novel research task involves dynamic modeling of noisy signals, modeling of large document collections and users’ interests, and information retrieval. Multimodal integration and natural language processing are needed to some extent as well. The project also involves a number of interesting challenges from the point of view of applying both kernel and Bayesian methods.

We consider reinforcement learning under the paradigm of online learning where the objective is good performance during the whole learning process. This is in contrast to the typical analysis of reinforcement learning where one is interested in learning a finally near-optimal strategy. We will conduct a mathematically rigorous analysis of reinforcement learning under this alternate paradigm and expect as a result novel and efficient learning algorithms. We believe that for intelligent interfaces the proposed online paradigm provides significant benefits as such an interface would deliver reasonable performance even early in the training process. The starting point for our analysis will be the method of upper confidence bounds which has already been very effective for simplified versions of reinforcement learning. To carry the analysis to realistic problems with large or continuous state spaces we will estimate the utility of states by value function approximation through kernel regression. Kernel regression is a well founded function approximation method related to support vector machines and holds significant promise for reinforcement learning. Finally we are interested in methods for reinforcement learning where no or only little external reinforcement is provided for the learning agent. Since useful external rewards are often hard to come by, we will investigate the creation of internal reward functions which drive the consolidation and the extension of learned knowledge, mimicking cognitive behaviour.