We study a practical extension to the Valiant model of machine learning from examples [v84]: the ... more We study a practical extension to the Valiant model of machine learning from examples [v84]: the presence of errors, possibly maliciously generated by an adversary, in the sample data. Recent papers have made progress in the Valiant model by providing algorithms for ...
We study the computational feasibility of learning boolean expressions from examples. Our goals a... more We study the computational feasibility of learning boolean expressions from examples. Our goals are to prove results and develop general techniques that shed light on the boundary between the classes of ex-pressions that are learnable in polynomial time and those that are ...
Search all the public and authenticated articles in CiteULike. Include unauthenticated results to... more Search all the public and authenticated articles in CiteULike. Include unauthenticated results too (may include "spam") Enter a search phrase. You can also specify a CiteULike article id (123456),. a DOI (doi:10.1234/12345678). or a PubMed Id (pmid:12345678). ...
We study a practical extension to the Valiant model of machine learning from examples [v84]: the ... more We study a practical extension to the Valiant model of machine learning from examples [v84]: the presence of errors, possibly maliciously generated by an adversary, in the sample data. Recent papers have made progress in the Valiant model by providing algorithms for ...
A new class of distances appropriate for measuring similarity relations between sequences, say on... more A new class of distances appropriate for measuring similarity relations between sequences, say one type of similarity per distance, is studied. We propose a new "normalized information distance," based on the noncomputable notion of Kolmogorov complexity, and show that it is in this class and it minorizes every computable distance in the class (that is, it is universal in that it discovers all computable similarities). We demonstrate that it is a metric and call it the similarity metric . This theory forms the foundation for a new practical tool. To evidence generality and robustness, we give two distinctive applications in widely divergent areas using standard compression programs like gzip and GenCompress. First, we compare whole mitochondrial genomes and infer their evolutionary history. This results in a first completely automatic computed whole mitochondrial phylogeny tree. Secondly, we fully automatically compute the language tree of 52 different languages.
We study a practical extension to the Valiant model of machine learning from examples [v84]: the ... more We study a practical extension to the Valiant model of machine learning from examples [v84]: the presence of errors, possibly maliciously generated by an adversary, in the sample data. Recent papers have made progress in the Valiant model by providing algorithms for ...
We study the computational feasibility of learning boolean expressions from examples. Our goals a... more We study the computational feasibility of learning boolean expressions from examples. Our goals are to prove results and develop general techniques that shed light on the boundary between the classes of ex-pressions that are learnable in polynomial time and those that are ...
Search all the public and authenticated articles in CiteULike. Include unauthenticated results to... more Search all the public and authenticated articles in CiteULike. Include unauthenticated results too (may include "spam") Enter a search phrase. You can also specify a CiteULike article id (123456),. a DOI (doi:10.1234/12345678). or a PubMed Id (pmid:12345678). ...
We study a practical extension to the Valiant model of machine learning from examples [v84]: the ... more We study a practical extension to the Valiant model of machine learning from examples [v84]: the presence of errors, possibly maliciously generated by an adversary, in the sample data. Recent papers have made progress in the Valiant model by providing algorithms for ...
A new class of distances appropriate for measuring similarity relations between sequences, say on... more A new class of distances appropriate for measuring similarity relations between sequences, say one type of similarity per distance, is studied. We propose a new "normalized information distance," based on the noncomputable notion of Kolmogorov complexity, and show that it is in this class and it minorizes every computable distance in the class (that is, it is universal in that it discovers all computable similarities). We demonstrate that it is a metric and call it the similarity metric . This theory forms the foundation for a new practical tool. To evidence generality and robustness, we give two distinctive applications in widely divergent areas using standard compression programs like gzip and GenCompress. First, we compare whole mitochondrial genomes and infer their evolutionary history. This results in a first completely automatic computed whole mitochondrial phylogeny tree. Secondly, we fully automatically compute the language tree of 52 different languages.
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Papers by Ming Li