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PERSETUJUAN PERBATASAN INDONESIA-PAPUA NIUGINI
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| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Analisa |
| Volume / Edisi | : | IX-9, SEPTEMBER (No. 9) |
| Halaman | : | 812-823 |
| Abstrak | : | - |
DIPLOMASI DAN INDIA KEPENTINGAN NASIONAL: INDIA DAN KRISIS INDOCINA
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Analisa |
| Volume / Edisi | : | IX-9, SEPTEMBER (No. 9) |
| Halaman | : | 802-811 |
| Abstrak | : | - |
ENN: Extended Nearest Neighbor Method for Pattern Recognition
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | IEEE Computational intelligence |
| Volume / Edisi | : | 10 (No. 3) |
| Halaman | : | 52-60 |
| Abstrak | : | This article introduces a new supervised classification method - the extended nearest neighbor (ENN) - that predicts input patterns according to the maximum gain of intra-class coherence. Unlike the classic knearest neighbor (KNN) method, in which only the nearest neighbors of a test sample are used to estimate a group membership, the ENN method makes a prediction in a "two-way communication" style: it considers not only who are the nearest neighbors of the test sample, but also who consider the test sample as their nearest neighbors. By exploiting the generalized class-wise statistics from all training data by iteratively assuming all the possible class memberships of a test sample, the ENN is able to learn from the global distribution, therefore improving pattern recognition performance and providing a powerful technique for a wide range of data analysis applications. |
LANGKAH-LANGKAH PENYELESAIAN MASALAH KAMBOJA
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Analisa |
| Volume / Edisi | : | IX-9, SEPTEMBER (No. 9) |
| Halaman | : | 789-801 |
| Abstrak | : | - |
Learning Distributions of Image Features by Interactive Fuzzy Lattice Reasoning in Pattern Recognition Applications
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | IEEE Computational intelligence |
| Volume / Edisi | : | 10 (No. 3) |
| Halaman | : | 42-51 |
| Abstrak | : | This paper describes the recognition of image patterns based on novel representation learning techniques by considering higher-level (meta-)representations of numerical data in a mathematical lattice. In particular, the interest here focuses on lattices of (Type-1) Intervals' Numbers (INs), where an IN represents a distribution of image features including orthogonal moments. A neural classifier, namely fuzzy lattice reasoning (flr) fuzzy-ARTMAP (FAM), or flrFAM for short, is described for learning distributions of INs; hence, Type-2 INs emerge. Four benchmark image pattern recognition applications are demonstrated. The results obtained by the proposed techniques compare well with the results obtained by alternative methods from the literature. Furthermore, due to the isomorphism between the lattice of INs and the lattice of fuzzy numbers, the proposed techniques are straightforward applicable to Type-1 and/or Ty??-2 fuzzy systems. The far-reaching potential for deep learning in big data applications is also discussed. |
DIMENSI-DIMENSI KEAMANAN ASIA TENGGARA
| Pengarang | : | Jusuf Wanandi |
| Nama Majalah/Jurnal | : | Analisa |
| Volume / Edisi | : | IX-9, SEPTEMBER (No. 9) |
| Halaman | : | 771-788 |
| Abstrak | : | - |
Semi-Random Projection for Dimensionality Reduction and Extreme Learning Machine in High-Dimensional Space
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | IEEE Computational intelligence |
| Volume / Edisi | : | 10 (No. 3) |
| Halaman | : | 30-41 |
| Abstrak | : | -Random Projection (RP) is a popular technique for dimensionality reduction because of its high computational efficiency. However, RP may not yield highly discriminative low-dimensional space to produce best pattern classification performance since the random transformation matrix of RP is independent of data. In this paper, we propose a Semi-Random Projection (SRP) framework, which takes the merit of random feature sampling of RP, but employs learning mechanism in the determination of the transformation matrix. One advantage of SRP is that it achieves a good balance between computational complexity and classification accuracy. Another advantage of SRP is that multiple SRP modules can be stacked to form a deep learning architecture for compact and robust feature learning. In addition, based on the insight on the relationship between RP and Extreme Learning Machine (ELM), the SRP is applied to ELM to derive Partially Connected ELM (PC-ELM). The hidden nodes of PC-ELM are more discriminative and hence a smaller number of nodes are needed. Experiments on two real-world text corpus, i.e., 20 Newsgroups and Farms Ads., verify the effectiveness and efficiency of the proposed SRP. Experimental results also show that PC-ELM outperforms ELM for high-dimensional data. |
PERMASALAHAN KIK DAN KMKP
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Analisa |
| Volume / Edisi | : | IX-8, AGUSTUS (No. 8) |
| Halaman | : | 748-766 |
| Abstrak | : | - |
An Effective Missing-Data Estimation Approach for Small-Size Image Sequences
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | IEEE Computational intelligence |
| Volume / Edisi | : | 10 (No. 3) |
| Halaman | : | 10-29 |
| Abstrak | : | - |
New Trends of Learning in Computational Intelligence (Part II)
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | IEEE Computational intelligence |
| Volume / Edisi | : | 10 (No. 3) |
| Halaman | : | 8 |
| Abstrak | : | - |