Reverse Hypothesis Machine Learning: A Practitioner's Perspective
ມ.ນ. 2017 · Intelligent Systems Reference Library ຫົວທີ 128 · Springer
ປຶ້ມອີບຸກ
138
ໜ້າ
reportບໍ່ໄດ້ຢັ້ງຢືນການຈັດອັນດັບ ແລະ ຄຳຕິຊົມ ສຶກສາເພີ່ມເຕີມ
ກ່ຽວກັບປຶ້ມ e-book ນີ້
This book introduces a paradigm of reverse hypothesis machines (RHM), focusing on knowledge innovation and machine learning. Knowledge- acquisition -based learning is constrained by large volumes of data and is time consuming. Hence Knowledge innovation based learning is the need of time. Since under-learning results in cognitive inabilities and over-learning compromises freedom, there is need for optimal machine learning. All existing learning techniques rely on mapping input and output and establishing mathematical relationships between them. Though methods change the paradigm remains the same—the forward hypothesis machine paradigm, which tries to minimize uncertainty. The RHM, on the other hand, makes use of uncertainty for creative learning. The approach uses limited data to help identify new and surprising solutions. It focuses on improving learnability, unlike traditional approaches, which focus on accuracy. The book is useful as a reference book for machine learning researchers and professionals as well as machine intelligence enthusiasts. It can also used by practitioners to develop new machine learning applications to solve problems that require creativity.
ໃຫ້ຄະແນນ e-book ນີ້
ບອກພວກເຮົາວ່າທ່ານຄິດແນວໃດ.
ອ່ານຂໍ້ມູນຂ່າວສານ
ສະມາດໂຟນ ແລະ ແທັບເລັດ
ຕິດຕັ້ງ ແອັບ Google Play Books ສຳລັບ Android ແລະ iPad/iPhone. ມັນຊິ້ງຂໍ້ມູນໂດຍອັດຕະໂນມັດກັບບັນຊີຂອງທ່ານ ແລະ ອະນຸຍາດໃຫ້ທ່ານອ່ານທາງອອນລາຍ ຫຼື ແບບອອບລາຍໄດ້ ບໍ່ວ່າທ່ານຈະຢູ່ໃສ.
ແລັບທັອບ ແລະ ຄອມພິວເຕີ
ທ່ານສາມາດຟັງປຶ້ມສຽງທີ່ຊື້ໃນ Google Play ໂດຍໃຊ້ໂປຣແກຣມທ່ອງເວັບຂອງຄອມພິວເຕີຂອງທ່ານໄດ້.
eReaders ແລະອຸປະກອນອື່ນໆ
ເພື່ອອ່ານໃນອຸປະກອນ e-ink ເຊັ່ນ: Kobo eReader, ທ່ານຈຳເປັນຕ້ອງດາວໂຫຼດໄຟລ໌ ແລະ ໂອນຍ້າຍມັນໄປໃສ່ອຸປະກອນຂອງທ່ານກ່ອນ. ປະຕິບັດຕາມຄຳແນະນຳລະອຽດຂອງ ສູນຊ່ວຍເຫຼືອ ເພື່ອໂອນຍ້າຍໄຟລ໌ໄໃສ່ eReader ທີ່ຮອງຮັບ.
