  In this thesis we examined several multimodal feature extraction and learning
methods for retrieval and classification purposes. We reread briefly some
theoretical results of learning in Section 2 and reviewed several generative
and discriminative models in Section 3 while we described the similarity kernel
in Section 4. We examined different aspects of the multimodal image retrieval
and classification in Section 5 and suggested methods for identifying quality
assessments of Web documents in Section 6. In our last problem we proposed
similarity kernel for time-series based classification. The experiments were
carried over publicly available datasets and source codes for the most
essential parts are either open source or released. Since the used similarity
graphs (Section 4.2) are greatly constrained for computational purposes, we
would like to continue work with more complex, evolving and capable graphs and
apply for different problems such as capturing the rapid change in the
distribution (e.g. session based recommendation) or complex graphs of the
literature work. The similarity kernel with the proper metrics reaches and in
many cases improves over the state-of-the-art. Hence we may conclude generative
models based on instance similarities with multiple modes is a generally
applicable model for classification and regression tasks ranging over various
domains, including but not limited to the ones presented in this thesis. More
generally, the Fisher kernel is not only unique in many ways but one of the
most powerful kernel functions. Therefore we may exploit the Fisher kernel in
the future over widely used generative models, such as Boltzmann Machines
[Hinton et al., 1984], a particular subset, the Restricted Boltzmann Machines
and Deep Belief Networks [Hinton et al., 2006]), Latent Dirichlet Allocation
[Blei et al., 2003] or Hidden Markov Models [Baum and Petrie, 1966] to name a
few.
