Progressing from initially ambiguous functional analyses: Three case examples

Learning from Ambiguous Examples

Learning From Ambiguous Examples

Current inductive learning systems are not well suited to learning from ambiguous examples. We say that an example is ambiguous if it has multiple interpretations, only one of which may be valid. Some domains in which ambiguous learning problems can be found are natural language processing (NLP) and computer vision. An example of an ambiguous training instance with two interpretations is shown ...

Concept Learning from (Very) Ambiguous Examples

We investigate here concept learning from incomplete examples, denoted here as ambiguous. We start from the learning from interpretations setting introduced by L. De Raedt and then follow the informal ideas presented by H. Hirsh to extend the Version space paradigm to incomplete data: a hypothesis has to be compatible with all pieces of information provided regarding the examples. We propose an...

Correction: Rapidly Progressing Myelodysplastic Syndrome Initially Presenting as Acute Leukemia

[This corrects the article DOI: 10.7759/cureus.1096.].

Rapidly Progressing Myelodysplastic Syndrome Initially Presenting as Acute Leukemia

Myelodysplastic syndrome (MDS) refers to a group of various stem cell disorders, characterized by dysplastic and ineffective production in one or more cell lines. In general, MDS tends to present slowly over months to years and is commonly detected with routine bloodwork by primary care physicians. Patients may be asymptomatic and depending on age, comorbidities and risk classification of MDS m...