Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  consider two ll-studied problems regarding attribute efficient learning: learning decision lists and learning parity functions. First,  give an algorithm for learning decision lists of length k over n variables using 2O(k1/3) log n examples and time nO(k1/3). This is the first algorithm for learning decision lists that has both subexponential sample complexity and subexponential running time in the relevant parameters. Our approach is based on a new construction of low degree, low ight polynomial threshold functions for decision lists. For a wide range of parameters our construction matches a lor bound due to Beigel for decision lists and gives an essentially optimal tradeoff beten polynomial threshold function degree and ight.#R##N##R##N#Second,  give an algorithm for learning an unknown parity function on k out of n variables using O(n1-1/k) examples in poly(n) time. For k=o(log n) this yields the first polynomial time algorithm for learning parity on a superconstant number of variables with sublinear sample complexity.  also give a simple algorithm for learning an unknown length-k parity using O(k log n) examples in nk/2 time, which improves on the naive nk time bound of exhaustive search.
['Adam R. Klivans', 'Rocco A. Servedio']
I am writing a research paper on a idea Toward Attribute Efficient Learning of Decision Lists and Parities
[{"from": "human", "value": "I am writing a research paper on a idea Toward Attribute Efficient Learning of Decision Lists and Parities"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  consider two ll-studied problems regarding attribute efficient learning: learning decision lists and learning parity functions. First,  give an algorithm for learning decision lists of length k over n variables using 2O(k1/3) log n examples and time nO(k1/3). This is the first algorithm for learning decision lists that has both subexponential sample complexity and subexponential running time in the relevant parameters. Our approach is based on a new construction of low degree, low ight polynomial threshold functions for decision lists. For a wide range of parameters our construction matches a lor bound due to Beigel for decision lists and gives an essentially optimal tradeoff beten polynomial threshold function degree and ight.#R##N##R##N#Second,  give an algorithm for learning an unknown parity function on k out of n variables using O(n1-1/k) examples in poly(n) time. For k=o(log n) this yields the first polynomial time algorithm for learning parity on a superconstant number of variables with sublinear sample complexity.  also give a simple algorithm for learning an unknown length-k parity using O(k log n) examples in nk/2 time, which improves on the naive nk time bound of exhaustive search."}]