Search Results (5)
Scalable neuro-symbolic machine learning
With the resurgence of neural network-based learning in the last decade, machine learning methods are becoming critical components of many real-life intelligent systems. However, while being able to learn effectively and at scale, such systems are often non-interpretable and unable to exploit existing symbolic background knowledge. The paradigm that offers …
Text mining for cross-domain knowledge discovery
One of the prevailing tendencies in science is research over-specialization, resulting in deep but relatively isolated islands of knowledge. Due to the huge amounts of scientific information produced at an increasingly fast pace, it has become difficult to follow even the specific literature limited to a single domain of specialization. …
Knowledge discovery in a service-oriented data mining environment
The thesis addresses the development of novel knowledge discovery scenarios in a modern data mining platform by utilising principles of service-oriented architecture with web services, interactive scientific workflows, knowledge discovery ontologies and automated construction of data mining workflows. We present the developed Orange4WS platform which upgrades Orange, a mature open-source …
Supervised Descriptive Rule Induction
The goal of knowledge discovery in databases is to construct models or discover interesting patterns in data. Model construction and pattern discovery are frequently performed by rule learning, as the induced rules are easy to be interpreted by human experts. The standard classification rule learning task is to induce classification/prediction …
Functional interpretation of gene expression data
Microarrays are at the center of a revolution in biotechnology, allowing researchers to simultaneously monitor the expression of tens of thousands of genes. The final aim of a typical microarray experiment is to find a molecular explanation for a given macroscopic observation (e.g., which pathways are affected by the loss …